About PayPay India Referral Hiring 

▼About PayPay India Referral Hiring 

At PayPay India, we are strengthening our referral hiring activities (hiring through referrals from employees)! To be eligible for this program, applications must be made based on referral from an acquaintance or a friend who is currently working for PayPay India or PayPay corporation in Japan.

If you can relate to the "PayPay 5 senses" which are important work values for PayPay group employees, you will surely enjoy working in this environment! Let's create the No. 1 fintech company together!

To apply for a job opening, please click on the “ENTRY” button on this page and fill in the necessary information. Our Hiring team will be in touch with you soon!

*1 To apply via this program, you need to fill in the name of the employee working at PayPay India Private Limited. or PayPay corporation in Japan. (the person who is referring you to the company).

*2 After your application is complete, we may ask the employee you have stated in the form to confirm whether or not he/she has introduced you to the job opening.

*3 This form must be completed by the applicant (no substitutions)

Current Job openings at PayPay India

1. Backend Engineer
2. Senior Backend Engineer
3. QA Engineer
4. Cloud Infrastructure Engineer (AI/ML Ops)
5. Cloud Infrastructure Engineer (K8 & NW Specialist)
6. Product Designer (UX/UI)
7. Site Reliability Engineer
8. Senior Product Manager (Anti-Fraud)

▼About PayPay India

Six elements for the ideal candidates at PayPay Group

▼Six elements for the ideal candidates at PayPay Group

【Unparalleled speed】
Discover for yourself the important things that need to be done and implement ways to reach the best results at the fastest speed possible for the organization.

【Commitment】
As a professional, commit to the growth and business goals of the organization and create impactful results by your ownership.

【Logical thinking】
Think logically and structurally to bring real communication.

【Curiosity and questioning mind】
Keep your curiosity about new things and your challenges along with a continuous questioning mind and enjoy such circumstances in a future-oriented manner.

【Problem solving】
Take a proper approach towards both explicit and potential business/organization challenges to lead solutions involving others.

【True opinion and communication skill】
Communicate your opinions to others in a flat manner to ensure mutual understanding and to keep things moving forward.

Working Conditions 

Employment Status

• Full Time

Office Location

• Gurugram (Wework)
  

  Remarks

  The development center requires you to work in the Gurugram office to establish the strong core team.
  Remote / Hybrid will be introduced once we have achieved the goal.

Work Hours

• In principle, 10:00am-6:45pm (actual working hours: 7h45m + 1h break)

Holidays

• Every Sat/Sun/National holidays/Company-designated Special days

Paid leave

• PL (Privilege leave), CL (Casual Leave), Other special leaves

Salary

• Annual salary paid in 12 installments (monthly)
  Based on skills, experience, and abilities

Hiring Process

・Application Review

・Coding-test

・ 1 - 3 Interviews (online)

* An aptitude test, code challenge (online) and some assignments, reference check may be happening depending on the positions you apply.
Those details will be explained after the application review.

We are looking for a Solutions Engineer – Lead Engineer to join our Corporate Technology team. As a Solutions Engineer, you will be expected to leverage your extensive experience and technical expertise to design, implement, and optimize Windows-based solutions in enterprise environments.

What You'll Do:

• Architectural Design: Lead the design and development of complex solutions, ensuring alignment with business objectives and architectural principles.
• AI observability for platforms and assistants. Instrument agents and automations with metrics for accuracy, drift, latency, containment, and user satisfaction; wire dashboards (Grafana/ELK) and feedback loops into CI/CD so models and prompts improve continuously—consistent with the JD’s observability focus
• Technical Leadership: Provide guidance and mentorship to engineering teams in the implementation of Windows-based solutions, ensuring best practices and adherence to industry standards.
• Solution Development: Collaborate with cross-functional teams to translate business requirements into scalable and secure Windows architectures, integrating with existing systems and technologies.
• Technology Evaluation: Research and evaluate emerging technologies and tools, recommending innovative solutions to enhance system performance, reliability, and security.
• Risk Management: Identify and mitigate risks associated with Windows-based solutions, ensuring compliance with regulatory requirements and security standards.
• Troubleshooting and Optimization: Investigate and resolve complex technical issues related to Windows infrastructure, optimizing performance, and minimizing downtime.
• Automation and DevOps: Knowledge of Gitops, Scripting and automation would be an essential part of the role. Knowledge of writing automations in either PowerShell/Python would be a mandatory requirement for this role.

What You’ll Need:

• 6+ years architecting and implementing enterprise infrastructure solutions with a proven track record of designing scalable, resilient, and secure systems including integration with Windows and cloud platforms.
• Expertise in Windows Server, Active Directory, Microsoft Azure/AWS virtualization (EC2, Azure VMs, Azure Virtual Desktop), and Microsoft Defender. Knowledge of macOS integration with enterprise identity platforms (Active Directory, Azure AD, Okta), including single sign-on (SSO) and federated authentication. Ability to design and optimize multi-platform architectures (Windows, macOS, Linux) for scalability, resilience, and security.
• Proficiency with Microsoft Intune for endpoint management (MDM/MAM) and understanding of hybrid architecture and cloud shared responsibility model. Proficiency in macOS device management using tools such as Jamf Pro, Kandji, or Apple Business Manager, including MDM/MAM strategies for hybrid and multi-platform environments. Experience with automation and scripting on macOS (e.g., Bash, Zsh, AppleScript, Python), enabling workflow optimization and cross-platform orchestration
• Strong troubleshooting skills for macOS infrastructure, including performance optimization, incident response, and root cause analysis.
• PowerShell and Python coding/scripting capabilities for automation and workflow development
• Experience with GitOps, CI/CD pipelines, and Infrastructure as Code (Terraform, Ansible, ARM templates)
• Proficiency in cloud-native and multi-cloud architectures (Kubernetes, Docker, serverless)
• Familiarity with observability and monitoring platforms (Prometheus, Grafana, ELK stack) and AI-driven automation
• Familiarity with Exchange Online, SharePoint, Microsoft Power Platform would be a plus
• Experience implementing zero trust solutions (Zscaler, CrowdStrike) would be a plus
• Relevant certifications such as Microsoft Certified: Azure Solutions Architect Expert, MCSE: Core Infrastructure, or AWS Solutions Architect would be a plus
• Good to have exposure with AI/ML integration, data analytics, and automation in enterprise environments.

Arcesium's Personal Data Privacy Notice for Candidates is linked here. 

Role Summary

We are hiring Senior Engineers who combine solid software engineering fundamentals with proven proficiency in AI-assisted development. This role is designed for engineers who have actively used AI coding tools (such as Cursor, Claude Code, Codex CLI only) for at least one year and can leverage these capabilities to ship higher-quality code faster. You will be embedded in product engineering squads, leading feature development, mentoring junior engineers, and championing AI-first development practices across the team.

What Makes This Role Different

This is not an AI research role - it is a core engineering role for engineers who use AI as a force multiplier. We expect you to:

• Use AI coding assistants daily to accelerate development without sacrificing code quality.
• Critically evaluate AI-generated code for correctness, security vulnerabilities, and maintainability.
• Share best practices for AI-assisted workflows with the broader engineering team.
• Help define team standards for responsible and effective AI tool adoption.

Key Responsibilities

1  Feature Engineering & Delivery

• Design, build, and ship production-quality features across the full stack or within your domain (backend, frontend, platform, or data).
• Leverage AI coding assistants to write, refactor, document, and test end-to-end code efficiently.
• Translate product requirements and technical specifications into clean, maintainable implementations.
• Lead feature squads for medium-complexity initiatives from design through deployment.
• Participate in and drive sprint planning, backlog grooming, and technical estimation sessions.

2  AI-Augmented Development Practices

• Champion the use of AI pair-programming tools (Copilot, Cursor, Claude, etc.) across the engineering org.
• Develop and document internal guidelines for effective AI prompt engineering within coding workflows.
• Conduct training sessions or knowledge-share presentations on maximising productivity with AI tools.
• Critically audit AI-generated code for logic errors, security issues, and performance anti-patterns before merging.
• Stay updated on the latest AI development tooling and evaluate new tools for team adoption.

3  Code Quality & Engineering Excellence

• Write testable, modular, and well-documented code following the team's style guides and design principles.
• Drive test-driven development (TDD) and maintain code coverage targets.
• Perform thorough code reviews with constructive feedback; uphold engineering standards.
• Identify and address technical debt proactively; contribute to architecture improvements.
• Participate in RCA (root cause analysis) for production incidents; implement lasting fixes.

4  Architecture & Technical Design

• Contribute to system design discussions, proposing solutions with appropriate trade-off analysis.
• Create and maintain technical design documents (TDDs), ADRs (Architecture Decision Records), and API contracts.
• Evaluate and adopt relevant third-party libraries, APIs, and SaaS tools with a security-first mindset.
• Collaborate with platform / DevOps teams on observability, CI/CD pipelines, and infrastructure-as-code.

5  Mentoring & Team Growth

• Mentor 1–2 mid-level engineers; conduct regular 1:1 technical coaching sessions.
• Onboard new team members by sharing codebase context, tooling setup, and team practices.
• Contribute to a culture of psychological safety, learning from failures, and continuous improvement.

6  Cross-functional Collaboration

• Work closely with Product Managers, Designers, and Operations to deliver delightful, high-quality user experiences.
• Coordinate with data and AI/ML teams to integrate model endpoints, APIs, and AI features into product surfaces.
• Participate in customer-facing escalations where deep technical expertise is required.

Required Qualifications

1  Education

• Bachelor's degree in Computer Science, Software Engineering, or a related field.
• Candidates without a degree but with a strong portfolio and demonstrable project history will be considered.

2  Experience

• 3 – 5 years of professional software engineering experience in a product-focused environment.
• Minimum 1 year of active, hands-on experience using AI coding tools in professional work (not just personal projects).
• Track record of shipping production features end-to-end with measurable business impact.

3  Technical Skills — Core Engineering

• Languages: Proficiency in at least one of — Python, TypeScript/JavaScript, Java, Go, or Kotlin.
• Web / API: REST, GraphQL, gRPC; familiarity with microservices and event-driven architectures.
• Databases: SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis, DynamoDB).
• Cloud: AWS, GCP, or Azure — compute, storage, serverless, and managed services.
• DevOps: Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins, CircleCI); IaC basics (Terraform/Pulumi).
• Testing: Unit, integration, and e2e testing frameworks; TDD/BDD familiarity.
• Version Control: Git, branching strategies (GitFlow, trunk-based), PR workflows.

4  AI Tooling Skills (Mandatory)

• Demonstrated proficiency with at least one AI coding assistant (Cursor IDE, Claude Code, Codex CLI).
• Ability to craft effective prompts for code generation, refactoring, debugging, and documentation tasks.
• Experience reviewing and validating AI-generated code for correctness and security.
• Understanding of limitations of LLM-based coding tools (hallucinations, context window, license risks).

5  Nice to Have

• Contributions to open-source projects.
• Experience building or integrating AI/ML features (e.g., calling LLM APIs, embedding models, RAG pipelines).
• Familiarity with agentic coding frameworks (LangChain, LlamaIndex, AutoGen, CrewAI).
• Experience in a startup or scale-up environment with rapid iteration cycles.
• Knowledge of web security (OWASP Top 10, secure coding practices).

Behavioural Competencies

• AI-First Mindset: Naturally reaches for AI tools to accelerate tasks while maintaining engineering rigour.
• Ownership: Takes end-to-end accountability — from design to deployment to monitoring.
• Pragmatism: Balances perfect with shipped; knows when to iterate vs. over-engineer.
• Mentorship: Invests in the growth of peers and junior colleagues.
• Communication: Writes clearly (code comments, docs, Slack) and articulates trade-offs confidently.
• Curiosity: Continuously explores new tools, languages, and architectural patterns.
• Collaboration: Works well in cross-functional squads; builds trust quickly.

Role Summary

We are seeking a Lead Engineer, AI Systems to serve as the technical anchor across our AI-augmented engineering organisation. This role bridges our Senior AI Coding Engineers, the AI/ML Developer (SLM Specialist), and the Agentic Systems intern cohort. You will set technical direction, own cross-cutting architectural decisions, and champion the responsible, high-impact adoption of AI-assisted development practices across the entire engineering function.

You will remain hands-on with code while simultaneously shaping how the wider team builds, evaluates, and ships AI-powered products.

Key Responsibilities

1  Technical Vision & Architecture

• Define and own the end-to-end technical architecture for AI Systems — spanning product feature surfaces, model inference APIs, and agentic toolchains.
• Drive Architecture Decision Records (ADRs), system design reviews, and RFC processes across squads.
• Establish standards for integrating SLM/LLM model endpoints into product surfaces built by the Senior Engineering team.
• Evaluate emerging AI infrastructure patterns (RAG, agentic orchestration, vector stores, model serving) and guide adoption decisions.
• Own the technical roadmap for AI tooling, developer productivity, and model integration layers.

2  Cross-Squad Technical Leadership

• Act as the primary technical liaison between the AI/ML Developer (model side) and Senior Engineers (product side), ensuring smooth API contracts, evaluation loops, and deployment handoffs.
• Provide technical direction to Agentic Systems interns, reviewing designs, code, and agentic pipeline implementations.
• Unblock senior engineers on hard architectural or integration challenges that span squad boundaries.
• Run cross-team design reviews, architecture syncs, and engineering guild sessions.

3  AI-Augmented Engineering Excellence

• Define and maintain the organisation’s standards for AI-assisted development — covering context engineering, AI code review protocols, context management, and tool evaluation criteria.
• Maintain and evolve the internal AI tooling playbook (Cursor IDE, Claude Code, Codex CLI, and emerging tools).
• Evaluate new AI coding tools, agentic frameworks (LangChain, LlamaIndex, CrewAI, AutoGen), and developer-productivity platforms; produce adoption recommendations with measured trade-offs.
• Conduct structured audits of AI-generated code across squads for correctness, security, and maintainability.

4  Hands-On Engineering

• Remain an active contributor: own critical-path features, prototype architectural spikes, and build shared infrastructure components used across squads.
• Personally drive resolution of the most complex production incidents and root-cause analyses.
• Review and merge high-impact PRs; maintain the highest code review quality bar on the team.
• Own observability and reliability for AI inference and MLOps integration layers in production.

5  Mentoring & Talent Development

• Mentor Senior Engineers, the AI/ML Developer, and interns through technical coaching, design feedback, and stretch assignments.
• Lead hiring panels and technical interviews; help define and uphold the engineering hiring bar.
• Contribute to onboarding frameworks that embed AI-first practices from day one.
• Model a culture of rigorous experimentation, psychological safety, and continuous improvement.

6  Stakeholder & Product Collaboration

• Partner with the Director of Engineering and Product leadership to translate product strategy into a phased technical roadmap.
• Present architectural proposals and trade-off analyses to engineering leadership and executive stakeholders as required.
• Coordinate with DevOps/Platform teams on GPU/TPU compute provisioning, CI/CD for model pipelines, and cloud cost optimisation.

Required Qualifications

1  Education

• Bachelor’s or Master’s degree in Computer Science, or a related field.
• Candidates without a degree but with a compelling portfolio demonstrating scope and impact at staff/lead level will be considered.

2  Experience

• 6 – 8 years of professional software engineering experience in product-focused environments.
• Minimum 2 years in a formal or de-facto technical lead / staff engineer capacity across multiple squads or systems.
• Minimum 8-12 months of active, hands-on experience with AI coding tools in a professional engineering setting.
• Proven track record of shipping production systems with measurable business impact at scale.
• Demonstrated experience collaborating closely with ML/AI model teams on integration, deployment, and evaluation.

3  Technical Skills — Core Engineering

• Languages: Expert proficiency in at least two of — Python, TypeScript/JavaScript, Go.
• Architecture: Microservices, event-driven systems, API design (REST, GraphQL, gRPC), distributed systems fundamentals.
• Databases: SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis); vector databases (FAISS, Pinecone, Weaviate).
• Cloud: AWS, GCP, or Azure — compute, storage, serverless, managed ML services (SageMaker, Vertex AI).
• DevOps: Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins); IaC (Terraform/Pulumi); observability stacks.
• Testing: TDD/BDD; unit, integration, and e2e frameworks; model evaluation pipelines.

4  AI / ML Integration Skills (Mandatory)

• Demonstrated proficiency with AI coding assistants (Cursor IDE, Claude Code, Codex CLI) in daily professional workflows.
• Experience designing and consuming LLM/SLM inference APIs; understanding of model serving, latency, and cost trade-offs.
• Hands-on familiarity with RAG architectures, vector stores, and retrieval pipelines.
• Ability to define and enforce AI code quality standards across an engineering team.
• Understanding of LLM limitations: hallucinations, context window constraints, prompt injection risks, and licensing considerations.

5  Technical Skills — Nice to Have

• Experience with SLM/LLM training or fine-tuning pipelines (SFT, RLHF, LoRA/QLoRA).
• Familiarity with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) and multi-step reasoning pipelines.
• Contributions to open-source ML or developer tooling projects.
• Knowledge of OWASP Top 10, secure coding, and AI-specific security risks (prompt injection, model exfiltration).
• Prior experience in a high-growth startup or scale-up environment with rapid iteration cycles.

Behavioural Competencies

• AI-First Mindset: Defaults to AI tools to accelerate work while maintaining rigorous quality standards; actively pushes the team’s AI capability forward.
• Systems Thinking: Sees the full picture — how model outputs, product surfaces, and infrastructure interdepend; anticipates second-order effects of architectural choices.
• Ownership: Takes end-to-end responsibility from design through production; doesn’t hand off problems, solves them.
• Influence Without Authority: Earns trust through technical credibility; drives alignment through persuasion, data, and well-reasoned proposals.
• Communication: Writes clear design docs, ADRs, and post-mortems; articulates complex trade-offs for engineering peers and non-technical stakeholders.
• Mentorship: Actively invests in the growth of engineers at all levels; gives direct, constructive feedback.
• Pragmatism: Balances the ideal with the shipped; knows when to iterate vs. refactor vs. rewrite.
• Curiosity: Continuously explores emerging research, tools, and patterns — and brings those learnings back to the team.

Job Title: Senior Full Stack Machine Learning Engineer

About Us:

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting-edge technology to improve health outcomes and streamline healthcare processes. We are looking for a talented and experienced Senior Full Stack Machine Learning Engineer to join our dynamic team.

Job Description:

• As a Senior Full Stack Machine Learning Engineer, you will play a pivotal role in designing, developing, and deploying machine learning models that drive our healthcare solutions. You will work closely with data scientists, software engineers, and product managers to build scalable and robust machine learning systems. Your expertise will help us transform healthcare data into actionable insights, ultimately improving patient care and operational efficiency.

Key Responsibilities:

• Model Development: Design, implement, and optimize machine learning models for various healthcare applications, including predictive analytics, natural language processing, and generative AI.
• End-to-End Deployment: Develop and maintain the full lifecycle of machine learning solutions, from data preprocessing and model training to deployment and monitoring in production environments.
• Data Engineering: Collaborate with data engineers to build and maintain data pipelines, ensuring the availability of high-quality data for training and inference.
• Software Development: Write clean, efficient, and maintainable code for machine learning applications, ensuring seamless integration with existing systems.
• Performance Optimization: Continuously monitor and improve the performance of machine learning models and systems, addressing issues related to scalability, latency, and accuracy.
• Collaboration: Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.
• Mentorship: Provide guidance and mentorship to junior engineers, fostering a culture of continuous learning and innovation.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. Ph.D. is a plus.
• Experience: Minimum of 5 years of experience in machine learning, with a proven track record of deploying models in production environments.

Technical Skills:

• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Proficiency in building applications leveraging generative AI technologies which includes LLM’s, prompt engineering, Vector Databases, RAG architectures and transfer learning is a plus.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker, Kubernetes).
• Strong knowledge of data structures, algorithms, and software engineering best practices.
• Familiarity with big data technologies (e.g., Hadoop, Spark) and data pipeline tools (e.g., Airflow, Kafka).
• Experience with frontend and backend development, including frameworks such as React, Node.js, and Django.

Domain Knowledge: Understanding of healthcare data standards (e.g., HL7, FHIR) and regulations (e.g., HIPAA) is a plus.

Soft Skills:

• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities.
• Ability to work independently and as part of a team in a fast-paced environment.

What We Offer:

• Competitive salary and benefits package.
• Opportunity to work on impactful projects that improve healthcare outcomes.
• Collaborative and innovative work environment.
• Professional development and growth opportunities.
• Flexible work arrangements, including hybrid and remote options.

Join us in our mission to transform healthcare through technology! We look forward to hearing from you.

About the role: 

Turing is looking for people with LLM experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the Turing GenAI delivery organization and part of  a GenAI project. You will be required to work with a team of other Turing engineers across different skill sets. In the past, the Turing GenAI delivery organization has implemented industry leading multi-agent LLM systems, RAG systems, and Open Source LLM deployments for major enterprises. 

Roles & Responsibilities:

• Develop and optimize LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
• Codebase ownership: Maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.
• Cloud integration: Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.
• Cross-functional collaboration: Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.
• Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
• Continuous innovation: Stay abreast of the latest advancements in LLM research and generative AI, proposing and experimenting with emerging techniques to drive ongoing improvements in model performance.

Required skills:

• 8+ years of professional experience in building Machine Learning models & systems
• 1+ years of hands-on experience in how LLMs work & Generative AI (LLM) techniques particularly prompt engineering, RAG, and agents.
• Experience in driving the engineering team toward a technical roadmap.
• Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.
• Understanding of Cloud services, including Azure, GCP, or AWS
• Excellent communication skills to effectively collaborate with business SMEs

About the role: 

Turing is looking for people with LLM experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the Turing GenAI delivery organization and part of a GenAI project. You will be required to work with a team of other Turing engineers across different skill sets. In the past, the Turing GenAI delivery organization has implemented industry leading multi-agent LLM systems, RAG systems, and Open Source LLM deployments for major enterprises. 

Required skills

• 8 + years of professional, hands-on engineering experience, including 4–6+ years specializing in building and deploying AI/ML models and systems.
• 1+ year of experience in developing Generative AI (LLM) applications using techniques like prompt engineering, RAG, and/or agents.
• Expert in architecting GenAI applications/systems using various frameworks & cloud services
• Good proficiency in using various cloud services from Azure, GCP, or AWS for building the GenAI applications
• Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.
• Experience in driving the engineering team toward a technical roadmap.
• Excellent communication skills to effectively collaborate with business SMEs

Roles & Responsibilities

• Build the technical roadmap given a business requirement and own the delivery of the same.
• Lead the engineering team toward a technical roadmap and ensure timely execution of the roadmap to achieve customer satisfaction. 
• Develop and optimize LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
• Codebase ownership: Maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.
• Cloud integration: Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.
• Cross-functional collaboration: Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.
• Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
• Continuous innovation: Stay abreast of the latest advancements in LLM research and generative AI, proposing and experimenting with emerging techniques to drive ongoing improvements in model performance.

Education

• Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, or a related technical field.

About Us:

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting-edge technology to improve health outcomes and streamline healthcare processes.

Role overview

 As an MLOps Engineer, you will design, build, and operate the infrastructure and tooling that power end-to-end ML workflows on AWS, including SageMaker, Bedrock, and Snowflake Cortex. You will partner closely with data scientists, ML engineers, and platform teams to ensure models are reliable, secure, and scalable in production.

Key responsibilities

• Design, implement, and maintain CI/CD pipelines for ML models and data workflows using AWS-native services and infrastructure-as-code.
• Operationalize models built on SageMaker, Bedrock, and Snowflake Cortex, including feature pipelines, training, batch/real-time inference, and monitoring.
• Build and manage data pipelines and feature stores using services such as AWS Glue, Lambda, Step Functions, and Snowflake.
• Implement observability for ML systems (logging, metrics, tracing, drift/quality monitoring) and establish SLOs/SLAs for production ML services.
• Automate environment provisioning, configuration, and dependency management across dev, test, and production.
• Partner with security and compliance teams to ensure ML workloads meet healthcare, privacy, and regulatory standards (e.g., HIPAA).
• Collaborate with ML engineers and data scientists to productionize notebooks and prototypes into robust, maintainable services.
• Contribute to best practices, standards, and documentation for ML platform and operations across the organization.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.
• 4+ years of experience in software engineering, data engineering, or ML engineering with at least 2+ years focused on MLOps or ML platform work.
• Strong proficiency with Python and experience integrating ML libraries or frameworks (e.g., scikit-learn, TensorFlow, PyTorch) into production workflows.
• Hands-on expertise with AWS services relevant to MLOps: SageMaker, Bedrock, IAM, CloudWatch, ECR, ECS/EKS or Lambda, S3, Step Functions, and Glue.
• Experience with Snowflake (including Snowflake Cortex), SQL, and building secure, performant data pipelines into and out of Snowflake.
• Proficiency with CI/CD tools (e.g., GitHub Actions, GitLab CI, CodePipeline) and infrastructure-as-code (e.g., Terraform, CloudFormation, CDK).
• Familiarity with containerization and orchestration (Docker, Kubernetes) and event streaming tools (e.g., Kafka) is a plus.
• Knowledge of software engineering best practices, including testing, code reviews, version control, and design for reliability and scalability.
• Experience in regulated domains or with healthcare data standards and regulations is a plus (e.g., HIPAA, FHIR, HL7).

Soft skills and benefits

• Excellent problem-solving and analytical skills with a focus on reliability and automation.
• Strong communication and collaboration abilities, including working cross-functionally with engineering, data science, and product teams.
• Ability to work independently in a fast-paced environment
• Competitive salary and benefits package.
• Opportunity to work on impactful ML platforms that improve healthcare outcomes.
• Collaborative, innovative environment with professional development and growth opportunities.

About the Role

We're pioneering a new role focused exclusively on quality assurance for GenAI systems. As our AI QA Engineer, you'll develop testing strategies, evaluation frameworks, and quality metrics specifically designed for LLM-powered applications. This role requires a unique blend of QA expertise, understanding of GenAI behaviour, and automation skills

to ensure our AI features are reliable, accurate, and trustworthy

Key Responsibilities

• Design and implement comprehensive testing strategies for GenAI features including conversational AI, agentic systems, and LLM-powered workflows
• Develop automated test suites for prompt testing, including regression tests that detect unintended changes in model behaviour
• Create evaluation frameworks to measure GenAI quality across multiple dimensions (accuracy, relevance, safety, consistency, latency)
• Build and maintain test datasets and golden examples that represent diverse user scenarios and edge cases
• Implement monitoring and alerting systems to detect quality degradation in production GenAI features
• Perform adversarial testing to identify potential failures, hallucinations, biases, or security vulnerabilities in AI systems
• Collaborate with engineers to define acceptance criteria and quality gates for AI feature releases
• Develop tools and frameworks that make it easy for engineers to test their GenAI implementations
• Conduct user acceptance testing and gather feedback on AI feature performance from internal users
• Document testing procedures, known issues, and quality metrics in clear, accessible formats
• Partner with Product and Design teams to ensure AI features meet user experience standards
• Stay current with GenAI testing methodologies, tools, and industry best practices

Required Qualifications

• 3+ years of QA or test engineering experience, preferably with AI/ML systems.
• Strong understanding of GenAI technologies including LLMs, prompt engineering, and AI application patterns
• Experience with test automation frameworks and scripting (Python, JavaScript, Selenium, Pytest)
• Knowledge of software testing methodologies (functional, integration, regression, performance, security testing)
• Ability to design test cases and evaluation criteria for non-deterministic systems
• Strong analytical and problem-solving skills with attention to detail
• Experience with API testing tools (Postman, REST Assured) and backend testing
• Familiarity with CI/CD pipelines and automated testing integration
• Excellent communication skills for documenting issues and collaboration

Preferred Qualifications

• Experience testing conversational AI, chatbots, or agentic systems
• Knowledge of ML model evaluation metrics and techniques
• Familiarity with LLM evaluation frameworks (LangSmith, PromptFoo, Ragas)
• Experience with performance testing and load testing AI APIs
• Understanding of responsible AI principles including fairness, transparency, and safety testing
• Background in enterprise software or SaaS QA
• Experience with test management tools (TestRail, Zephyr, Jira)
• Knowledge of security testing methodologies for AI systems
• Scripting experience with Python, including working with LLM APIs

What Makes This Role Exciting

• Define QA practices for GenAI applications
• Work on cutting-edge AI technologies and help ensure they're reliable and trustworthy
• Shape quality standards that will impact millions of enterprise users
• Collaborate closely with engineers, data scientists, and product teams
• Grow expertise in a highly specialized and increasingly important domain
• Influence the entire AI product development lifecycle from design to release
• Join a team that values quality as a first-class concern, not an afterthought

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (6+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

About the Role

We're seeking a versatile GenAI Software Engineer who can work across the full stack of GenAI applications, from model integration and prompt engineering to building intuitive user interfaces. You'll build production-ready AI features that empower business users to leverage the power of GenAI within their planning workflows, requiring both deep ML knowledge and strong software engineering skills.

Key Responsibilities

• Develop end-to-end GenAI features including backend API services, model integration, model monitoring, evaluations and deployments
• Integrate and optimize LLMs for specific use cases in business planning, including prompt engineering, RAG implementation
• Build conversational interfaces and agentic workflows that make complex planning tasks accessible through natural language
• Implement evaluation frameworks to measure and improve GenAI feature quality, including accuracy, latency, and user satisfaction metrics
• Design and develop APIs that expose AI capabilities to Anaplan's platform and third-party integrations
• Optimize model inference pipelines for performance, cost, and scalability in production environments
• Implement monitoring, logging, and observability for GenAI systems to track usage, errors, and model behaviour
• Collaborate with data scientists to productionize ML models and forecasting algorithms
• Write comprehensive tests including unit tests, integration tests, and prompt regression suites
• Participate in code reviews, technical design discussions, and knowledge-sharing sessions
• Stay current with GenAI research and tools, evaluating new models and techniques for potential adoption

Required Qualifications

• 3+ years of software engineering experience with 2+ years of understanding on ML/AI systems.
• Strong programming skills in Python including understanding of ML frameworks (PyTorch, TensorFlow, Transformers)
• Understanding or experience of building and deploying LLM-powered applications in production
• Understanding or experience of front-end development with React, TypeScript, and modern web technologies
• Understanding of RESTful API design, microservices architecture, and cloud infrastructure
• Experience with prompt engineering, RAG systems
• Understanding of ML fundamentals including NLP, time-series analysis, or recommender systems
• Familiarity with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines
• Excellent problem-solving skills and attention to detail
• Bachelor’s degree in computer science, Machine Learning, or related field

Preferred Qualifications

• Experience with GenAI frameworks (LangChain, LlamaIndex, Haystack, or similar)
• Knowledge of vector databases (Pinecone, Weaviate, Qdrant) and embedding models
• Experience with model serving frameworks (vLLM, TensorRT, Ray)
• Background in forecasting, planning, or analytics applications
• Familiarity with Anaplan or similar enterprise planning platforms
• Experience with A/B testing and experimentation frameworks for AI features
• Contributions to open-source ML projects or research publications
• Experience with model observability tools (LangSmith, W&B, MLflow)

What Makes This Role Exciting

• Lead a greenfield team building transformative AI capabilities from the ground up
• Work on cutting-edge conversational and agentic AI technologies
• Build user-facing AI features that directly impact how businesses plan and make decisions
• Experiment with the latest GenAI models and techniques
• Collaborate with talented engineers, data scientists, and product designers
• Solve unique challenges at the intersection of AI and enterprise software
• Grow your skills in both ML engineering and full-stack development

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (6+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

About the Role

Here at Anaplan, we have reinvented how companies see, plan, and run their businesses. Our platform allows our customers to uncover new insights, connect their strategy to their plans, and work in ways they had not previously thought possible. Anaplan is the only scenario planning and analysis platform designed to optimize decision-making in today's complex business environment so that enterprises can outpace their competition and the market. By building connections and collaboration across organizational silos, our platform intelligently surfaces key insights — so businesses can make the right decisions, right now. We are seeking a talented AI QA Lead to guide our team and ensure the quality and reliability of our cutting-edge AI and machine learning solutions.

Key Responsibilities

• Lead, mentor, and grow high-performing QA Engineers across multiple stream aligned teams.
• Define and drive the overall QA strategy for our Forecasting project, with a focus on time-series forecasting.
• Oversee the design, development, and maintenance of automated test frameworks.
• Guide the team in implementing and executing robust test cases for our AI-powered features and APIs.
• Champion the use of BDD frameworks, such as Gherkin, to create clear, concise, and maintainable tests.
• Ensure the team effectively tests and validates RESTful APIs to meet high standards of quality and performance.
• Collaborate with product managers, data scientists, and engineering leads to align on quality standards and release criteria.
• Oversee the identification, documentation, and tracking of bugs and inconsistencies in our AI/ML systems.
• Drive improvements to our CI/CD pipeline to ensure that our AI models are continuously tested and deployed with confidence.

Required Qualifications

• A skilled QA Engineer with 8+ years of experience testing and assuring the quality of complex platform, infrastructure, or data-centric systems.
• Strong proficiency in Python.
• Proven experience leading or mentoring a QA team in a fast-paced technical environment.
• Deep understanding of machine learning concepts and a proven track record of testing ML models, particularly in the domain of time-series forecasting.
• Expertise with testing frameworks such as pytest.
• Strong experience with Behavior-Driven Development (BDD) and Gherkin.
• In-depth knowledge of API testing and validation.
• Proficiency in modern software development practices (testing, code review, CI/CD).
• A proven track record of delivering complex technical projects on time with high quality.
• Excellent leadership, communication, and interpersonal skills.

Preferred Qualifications

• Experience with performance, scalability, and security testing of AI systems.
• Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and their AI/ML services.
• Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes.
• Experience with strategic planning and roadmap development for a QA function.
• A Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

What Makes This Role Exciting

• You will lead a talented team at the forefront of AI innovation, working on a platform that is transforming how businesses make decisions.
• You will have a strategic impact, shaping the future of quality for our AI-powered products.
• You will be part of a collaborative and creative leadership team that values openness, authenticity, and inclusivity.
• We are a fast-growing company, and you will have ample opportunities for professional growth and to define your team's trajectory.

About the Role

Anaplan is looking for a skilled Python Machine Learning Developer to join our AI engineering organization. This is a crucial, hands-on developer role focused on building the robust backend systems that power our machine learning features. You are not just a developer; you are a software engineer who specializes in bringing ML models to life in a production environment. You will be responsible for designing, building, and deploying the APIs, data pipelines, and backend services necessary to integrate sophisticated machine learning capabilities into the Anaplan platform.

Key Responsibilities

• Design, build, and maintain scalable and reliable machine learning services and APIs using Python.
• Develop and productionize ML models, taking them from prototype to scalable, live systems.
• Construct and manage the data pipelines required for model training, validation, and inference.
• Build and interact with databases (both SQL and NoSQL) to store and retrieve data for ML applications.
• Write clean, high-quality, and well-tested code, adhering to software engineering best practices.
• Collaborate with data scientists, AI architects, and other engineers to integrate ML models into our products.
• Optimize applications for speed, scalability, and reliability.
• Contribute to our MLOps infrastructure to streamline the deployment and monitoring of machine learning models.

Required Qualifications

• Expert (10+ years) Python Programming: Strong, hands-on proficiency in Python for software development.
• Backend & API Development: Proven experience building and deploying backend services and RESTful APIs using frameworks like Flask, FastAPI, or Django.
• Database Proficiency: Solid experience with both SQL (e.g., PostgreSQL) and NoSQL (e.g., MongoDB, Redis) databases, including data modeling and query optimization.
• Machine Learning Libraries: Deep, practical knowledge of the core Python ML ecosystem:
• Data manipulation and analysis: Pandas, NumPy.
• Core ML frameworks: scikit-learn.
• Deep learning frameworks: Experience with TensorFlow or PyTorch.
• Strong understanding of software engineering principles, including data structures, algorithms, and design patterns.
• Experience with version control (Git) and CI/CD practices.
• Excellent problem-solving skills and the ability to write efficient, production-ready code.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with containerization technologies like Docker and orchestration with Kubernetes.
• Experience with MLOps tools and principles (e.g., MLflow, Kubeflow).
• Knowledge of large-scale data processing frameworks like Spark or Dask.

What Makes This Role Exciting

This role places you at the critical intersection of software engineering and machine learning. You are a builder, responsible for creating the tangible systems that make our AI intelligent. You will tackle complex engineering challenges, from API design to database performance, all within the context of delivering cutting-edge machine learning features. At Anaplan, you will see your code run at scale, directly impacting a product used by leading global enterprises and solving real-world business problems.

About Nanonets

Nanonets has a vision to help computers see the world starting with reading and understanding documents.Machine Learning (ML) is no longer a futuristic concept—it's a present-day powerhouse transforming the business landscape. Nanonets is at the forefront of this transformation, offering innovative ML solutions designed to make document related processes faster than ever before.

From automating data extraction processes to enhancing reconciliation, our solutions are designed to revolutionize workflows, optimize operations, and unlock untapped potential for our clients. Our client footprint spans across brands such as Toyota, Boston Scientific, Bill.com and Entergy to name a few enabling businesses across a myriad of industries to unlock the potential of their visual and textual data

We recently announced a series B round of $29 million in funding by Accel and are backed by the likes of existing investors including Elevation Capital & YCombinator. This infusion of capital underscores our commitment to driving innovation and expanding our reach in delivering cutting-edge AI solutions to businesses worldwide.

Read about the release here: Forbes | TechCrunch

We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity.

About the role

We’re looking for a technically strong, customer-facing problem solver to bridge product, engineering, and go-to-market teams. In this role, you’ll support the sales process by building tailored demos, validating technical requirements, and translating customer needs into practical workflows and solutions. You’ll work closely with Sales, Product, and Customer Success to ensure a smooth journey from discovery to onboarding, while also prototyping quick solutions to accelerate deal cycles.

What You’ll Do

• Build and customize high-impact customer demos by setting up tailored environments and workflows aligned to each prospect’s use case, including configuring agents, integrations, and realistic sample data.
• Partner closely with Sales on customer-facing calls to showcase technical capabilities, answer implementation questions, and translate product features into clear, customer-specific workflows and ROI narratives.
• Drive technical validation during discovery by deeply understanding customer requirements, assessing feasibility, and evaluating integration needs across systems such as EHRs, payer portals, telephony platforms, and APIs.
• Proactively identify edge cases, potential blockers, and any custom work required early in the sales cycle to ensure smooth downstream execution.
• Support activation and onboarding by collaborating with Activation/Customer Success and Product teams to deploy custom agents, configure workflows, fine-tune prompts, and assist with initial QA/UAT.
• Own rapid prototyping efforts by building lightweight workflows or custom solutions that help accelerate deal cycles and bridge gaps until fully productized features are available.

Required Qualifications -

• B.E/B.Tech or from a Tier 1 school.
• Minimum 2 years in a technical role.

Need to Have:

• Python
• Relational Database (any major DB)
• Understanding of APIs
• Git version control
• Excellent written & verbal communication skills

Good to Have:

• Experience with React, NodeJS, Golang, Cassandra, Kubernetes, Docker
• Exceptional verbal and written communication skills, including the ability to work with technical and non-technical audiences; as well as communicate effectively to individual contributors, management, and executive leadership.
• Strong communications skills, with the distinctive ability to empower and influence people without authority or a direct reporting relationship.

Your Impact

The Senior Software Engineer is responsible for project subsystems. They lead epics/initiatives, mentor more junior engineers, and contribute to architectural
decisions. They have a substantial impact on their team and, at times, across teams. They design and develop high-quality, scalable, and innovative software solutions in a SaaS environment, utilising cutting-edge technologies and methodologies, and ensuring they meet the current and future needs of the business and its customers

What You'll Do

• Technical Leadership and Mentorship: Provides technical leadership in project teams, mentors and guides more junior engineers, and contributes to technical decision-making.
• Performance Optimisation: Focuses on designing systems for high performance, scalability, and reliability.
• Stakeholder Engagement: Works with stakeholders, including product managers, designers, developers, and executives, to align architectural designs with business needs and customer expectations.
• Proficient Software Development: Designs, develops, and maintains sophisticated software solutions, ensuring they are efficient, scalable, and meet business requirements with architectural excellence. Responsible for System Design & Grooming for features for a subsystem.
• Code Quality and Optimisation: Upholds and enhances code quality through rigorous review processes, optimisation techniques, and adherence to best practices.
• Technical Problem Solving: Tackles complex technical challenges, employing a problem-solving approach with innovative solutions.
• Collaboration and Communication: Works effectively within the engineering team and cross-functional teams, collaborating with other engineering teams, product managers, and stakeholders, contributing to project planning and execution.
• Continuous Learning and Adaptation: Stays updated with the latest technology trends and advancements, continually enhancing skills and knowledge, bringing innovative ideas to the team
• Technical Documentation: Creates and maintains comprehensive technical documentation, ensuring clarity and accessibility of information.
• Data Platform Development: Designs, builds, and maintains scalable and robust data platforms to support analytics, machine learning, and business intelligence.
• Data Integration and ETL: Develops and optimises ETL (Extract, Transform, Load) processes and data integration pipelines. Ensure efficient data flow across various sources and systems
• Advanced File Processing: Develops extensible file processing pipelines that offer free self-service artefact generation. Researches and evaluates file processing software based on fidelity, reliability, and performance criteria. Models file processing outcomes within the observability framework to provide insights for engineering teams and business leaders. Develops engineering systems that facilitate rapid and minimally manual evaluation of file processor
• Data Management: Orchestrates scale-out data pipelines that optimise serverless and containerised compute to balance cost, latency, and duration. Contributes to algorithmically intensive data engines operating on streaming, large, or multi-tenant datasets. Troubleshot systems, identified root causes, and rectified bugs.

Who You Are

• 7-10 years of experience in software development, with a demonstrated track record in SaaS or similar environments.
• 5+ years of professional experience with each of the following: Service Oriented Architecture, Docker-based services, micro-services, API and Data Model Design or Implementation;
• Experience in playing key roles (for example: development, testing, design, product interfacing, etc) in significant software projects, showing an ability to work effectively both independently and as part of a team. Other preferred experience for the position, including length and in what capacity (e.g., operational, management level).
• Experience with developing and maintaining large-scale, high-availability software systems
• Experience with gRPC and Protocol Buffers for efficient, language-agnostic service-to-service communication
• Experience with designing and developing from a Security Perspective
• Experience selecting and extending 3rd party components (commercial or open source) that provide operational leverage
• Experience with Continuous Integration and Continuous Deployment (CI/CD) with an emphasis on a well-maintained testing pyramid
• Experience with Agile development methodologies and practices.
• Experience using feature or release toggles as a code branching strategy
• Experience with Contract-first designing and negotiating with other systems
• Experience contributing to technical documentation

Even Better If You Have...

Key 3-5 specific skills or knowledge required

• Knowledge of ElasticSearch, NoSql Stores, Kafka, Columnar Databases, DataFlow or Pipeline Systems
• Knowledge of Design, implementation, and operation of data-intensive distributed systems
• Understanding of microservices design patterns and principles, with experience in developing microservices using containerized tools like Docker
• Knowledge of system architecture, design patterns, and best practices in software development.
• Skilled in designing and operating software in a Cloud Provider, such as AWS, Azure, or GCP
• Skilled in cloud-based data storage, such as: Relational Databases, NoSQL Stores, data caches, etc.
• Skilled in analytical abilities and problem-solving, capable of addressing complex technical challenges.
• Ability to adapt to new technologies and methodologies, and a mindset geared towards innovation and continuous improvement.
• Effective communication and teamwork skills, essential for working in a dynamic and collaborative environment with various stakeholders and team members.
• Skilled in programming languages and frameworks commonly used in SaaS development. In particular, Java/Kotlin, Python, C#.
• Skilled in designing and developing APIs (including gRPC, GraphQL, and REST), basic understanding of HTTP/HTTPS protocols, and proficiency in API documentation. 
• Familiarity with server management, network configurations, and an understanding of cloud services (like AWS, Azure, or Google Cloud).
• Knowledge of various stages of the SDLC, including design, development, testing, and deployment.

Perks of DISCO

• Open, inclusive, and fun environment
• Benefits, including medical and dental insurance
• Competitive salary plus discretionary bonus
• Opportunity to be a part of a startup that is revolutionizing the legal industry
• Growth opportunities throughout the company

Tesco Media & Insight Platform is a partnership between Tesco, the UK’s largest grocery retailer, and dunnhumby. Fueled by first‑party behavioral data from over 23 million Clubcard holders, we help brands understand consumer behaviors and reach the right customers, at the right time, with the right message across the full marketing funnel. Our closed‑loop measurement connects what customers see with what they buy, helping brands Shape What Britain Buys and maximize the impact of every pound of media investment.

Roles and Responsibilities 

As a Senior AI Engineer, you will be a hands‑on technical contributor designing, building and operating production‑grade GenAI and agentic AI solutions that power Tesco Media & Insight Platform’s global products and services. You will work closely with the AI Engineering Manager, Product, Data Science and Engineering teams to turn business problems into secure, scalable, observable AI systems that deliver measurable value for brands and shoppers.

• Design and implement GenAI and agentic AI solutions on GCP, leveraging Vertex AI (models, endpoints, pipelines, vector search/RAG, Agent Builder) and core GCP services.
• Build AI agents and multi‑step agentic workflows, including tool calling, orchestration, and integrations with internal and external systems.
• Contribute to the design of MCP servers and agent‑to‑agent / agent‑to‑application protocols to enable safe, reliable automation of retail media and insight use cases.
• Work with the AI Engineering Manager and architects to define solution designs, interface contracts and non‑functional requirements (security, reliability, performance, cost).
• Implement security, privacy and governance controls in AI solutions, including safe prompt and tool design, data protection, secrets management and access control.
• Apply DevOps/MLOps practices: contribute to CI/CD pipelines for models, prompts and agents; use infrastructure as code; support environment management and automated testing.
• Implement observability for AI systems: instrument prompts, model calls, tools and agents with tracing, metrics and logs; build and maintain dashboards and alerts.
• Help define and run AI testing and evaluation strategies, including automated tests, offline and online evaluation, red‑teaming, regression suites and human‑in‑the‑loop review flows.
• Collaborate with Retail Media and Insight teams to understand use cases and data, and translate requirements into concrete AI components and services.
• Write high‑quality, well‑tested code in Python (and, where relevant, TypeScript/Java or similar) following team standards for readability, performance and reliability.
• Participate in code reviews, design reviews and incident post‑mortems, and contribute to continuous improvement of our AI engineering practices.
• Stay current on advances in LLMs, agentic frameworks and cloud AI services, and bring forward ideas for how they can be applied to Tesco Media & Insight Platform.

What you’ll need to succeed in the role

• Strong experience in software/data/ML engineering, with hands‑on delivery of AI/ML or LLM‑based solutions into production (not just prototypes).
• Practical experience on GCP and Vertex AI, including deploying models, building pipelines, and implementing RAG/vector search‑based solutions.
• Solid understanding of LLMs and foundation models: transformers at a high level, tokenization, embeddings, context windows, RAG, and the trade‑offs between fine‑tuning, adapters and prompt‑based approaches.
• Hands‑on experience building agentic workflows or AI agents (e.g. tool‑augmented chat, multi‑step workflows, or multi‑agent systems) using one or more modern frameworks or platforms.
• Good grasp of security and safety for AI systems, including prompt and tool design to reduce hallucinations and prompt injection, safe handling of sensitive data, and application‑level guardrails.
• Experience with DevOps/MLOps practices: CI/CD, version control, automated testing, infrastructure as code, monitoring and incident handling in a cloud environment.
• Strong programming skills in Python and experience building and consuming APIs; familiarity with microservices and event‑driven architectures is a plus.
• Ability to design and run evaluation and experimentation for AI features, including quantitative and qualitative measures and, where appropriate, human‑in‑the‑loop review.
• Strong problem‑solving skills, attention to detail, and a willingness to take ownership of complex technical challenges.
• Clear, proactive communication and the ability to collaborate effectively with product managers, data scientists, engineers and commercial stakeholders across locations.

Job Description

 PayPay is looking for a Cloud Infrastructure Engineer for PayPay's Cloud Based payment system. This role will be responsible for the engineering of Cloud Infrastructure Platforms to provide robust, reliable and cost-efficient Platform so that we can deliver the best experience for our growing customers base.

Primary Responsibilities

• Architect and build a robust, scalable and stable infrastructure
• Architect and build Infrastructure that is easily maintainable during patching and upgrades without downtime
• Architect and build Infrastructure with AWS regional level failure tolerance
• Work together with our Security Engineers to provision a secure Infrastructure
• Automations for AWS Deployments to ensure fast delivery of Cloud Services to our developers
• Provide through Automations means for developers to easily deploy resources on AWS

Tech Stack

• AWS:
  • VPC, EC2, ECS, EKS, Lambda, Cloudfront, MWAA, RDS, ElastiCache, DynamoDB, Opensearch, S3, CloudWatch, Cognito, SQS, KMS, Secret Manager, KMS, MSK, SES, Transfer Family, ClientVPN, Shield, WAF, GuardDuty, IAM Identity Center, Organization, WorkSpaces, CodeCommit, CodeBuild, CodeDeploy, CodePipeline, CloudFormation and other services
• Terraform, Github Actions, Prometheus, Grafana, Atlantis, ArgoCD, OpenTelemetry, Wiz, Backstage

Qualifications

• Looking 9+ years of technical and architectural experience in AWS-based infrastructure platforms
• Extensive technical hands-on experience with computing, networking, storage, and analytical services on Latest AWS Services
• Experience with Terraform based infrastructure as code (IAC) on AWS
• Experience in production level infrastructure change management and releases for high traffic & business-critical systems
• Experience in Cloud infrastructure and platform systems availability, performance and cost management
• Experience as an AWS administrator, including Multi AWS organization management, managing 50+ AWS accounts and 100+ users
• Strong understanding of cloud security best practices in Fintech Industry
• Demonstrated skill in programming and scripting languages, including Python, Shell Scripting, Go, and Rust
• Excellent oral, written, and interpersonal communication skills
• Proficiency in either English or Japanese

Preferred Qualifications

• Certified at the AWS Professional or Specialty level
• Experience in payment industry compliance standards such as PCI DSS 
• Bachelor’s degree and above in a Computer Science related field
• Experience with other cloud service providers (e.g GCP, Azure)
• Experience with Kubernetes (CKA, CKAD or CKS)
• Experience in AI/ML infrastructure design and operations
• ​​Experience using and contributing to Open Source tools
• Published papers / blogs / articles
• Bilingual in English and Japanese is nice to have, but not required
• JLPT Level 3 or above
• Leverage AI/LLM-based tools in daily workflows (e.g., code development, reviews, testing, debugging, documentation), while ensuring human oversight, judgment, and accountability drive the final product.

Remarks

＊Please note that you cannot apply for PayPay (Japan-based jobs) or other positions in parallel or in duplicate.

PayPay 5 senses

• Please refer PayPay 5 senses to learn what we value at work.

Working Conditions 

Employment Status

• Full Time

Office Location

• Gurugram (Wework)

　　※The development center requires you to work in the Gurugram office to establish the strong core team.
　　　

About Us:

Inovalon is a leading healthcare technology company dedicated to revolutionizing the healthcare industry through innovative AI and machine learning solutions. Our mission is to leverage cutting-edge technology to improve health outcomes and streamline healthcare processes. We are seeking a highly skilled Senior Machine Learning SDET to ensure the quality, reliability, and safety of our ML-powered healthcare products.

Job Description:

As a Senior Machine Learning SDET, you will be responsible for designing, implementing, and maintaining robust testing frameworks and quality strategies for machine learning systems across their entire lifecycle. You will collaborate closely with data scientists, ML engineers, software engineers, and product managers to validate models, data pipelines, and ML-driven services in production environments. Your work will help ensure that our ML solutions are accurate, performant, secure, and compliant with healthcare standards, ultimately improving patient care and operational efficiency.

Key Responsibilities:

• Test Strategy for ML Systems: Define and own the end-to-end test strategy for ML models, data pipelines, and services, including functional, performance, regression, and reliability testing.
• ML Model Validation: Design automated tests to validate model behavior (e.g., accuracy, drift detection, bias, stability, and robustness) across training, staging, and production environments.
• Data Quality & Pipeline Testing: Build automated tests and monitoring for data pipelines to ensure schema integrity, data completeness, data freshness, and correctness for training and inference.
• Test Automation Frameworks: Develop and maintain scalable test automation frameworks and tools for APIs, microservices, and ML workflows, integrating them into CI/CD pipelines.
• Performance & Scalability: Design and execute load and performance tests for ML inference services and batch jobs, focusing on latency, throughput, and resource utilization.
• Monitoring & Observability: Collaborate with ML and platform engineers to implement monitoring, logging, and alerting for ML systems, including model performance and data drift.
• Shift-Left Quality: Embed quality practices early in the ML development lifecycle, including testable model design, test data generation, and automated validation in CI.
• Tooling & Infrastructure: Leverage and extend existing tools (e.g., Python test frameworks, cloud services, containerization, big data tools) to support robust ML testing.
• Collaboration: Work closely with cross-functional teams to understand business and regulatory requirements and translate them into verifiable test scenarios and acceptance criteria.
• Mentorship: Provide guidance and mentorship to engineers on best practices for testing ML and data-intensive systems, fostering a culture of quality and continuous improvement.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field; Ph.D. is a plus.
• Experience: Minimum of 5 years of experience in software test engineering or SDET roles, with at least 3 years focused on testing machine learning, data, or distributed backend systems in production.

Technical Skills:

• Strong proficiency in Python and testing frameworks such as pytest, unittest, or similar, with experience testing ML code and data pipelines.
• Hands-on experience with ML libraries and ecosystems (e.g., TensorFlow, PyTorch, scikit-learn) sufficient to design model validation and testing strategies.
• Hands-on experience with generative AI concepts (LLMs, RAG, vector databases, prompt engineering, and transfer learning) and their testing implications is a strong plus.
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization/orchestration (Docker, Kubernetes) for deploying and testing ML services.
• Strong knowledge of data structures, algorithms, and software engineering best practices, including code reviews, version control, and CI/CD.
• Experience building and integrating automated tests for REST/gRPC APIs and microservices, including contract and integration testing.
• Experience with performance and load testing tools and techniques for backend and ML inference services.
• Experience with observability and monitoring tools (e.g., Prometheus, Grafana, ELK, or cloud-native equivalents) for tracking model and system health.

Domain Knowledge:

• Understanding of healthcare data standards (e.g., HL7, FHIR) and regulations (e.g., HIPAA) is a plus.
• Experience testing systems in regulated, high-compliance, or safety-critical environments is highly desirable.

Soft Skills:

• Excellent problem-solving and analytical skills, with a strong focus on edge cases, failure modes, and risk assessment.
• Strong communication and collaboration abilities, with comfort working across ML, data, product, and platform teams.
• Ability to work independently and as part of a team in a fast-paced environment, balancing quality, speed, and pragmatism.

What We Offer:

• Competitive salary and benefits package.
• Opportunity to work on impactful ML-driven healthcare products that improve patient outcomes and operational efficiency.
• Collaborative and innovative work environment.
• Professional development and growth opportunities in ML quality engineering and SDET leadership.
• Flexible work arrangements, including hybrid and remote options.

Join us in ensuring that cutting-edge AI and machine learning solutions are not only powerful but also safe, reliable, and trustworthy in healthcare. We look forward to hearing from you.

The Role

We’re looking for a seasoned Staff Machine Learning Engineer to shape and scale the backbone of our production ML ecosystem. In this role, you will architect high-performing ML systems that power our geospatial intelligence platform, transforming large-scale satellite and aerial imagery into actionable insights. You’ll lead end-to-end ownership—from model deployment and MLOps to infrastructure design—while partnering closely with data science, platform engineering, and product teams to deliver reliable, scalable, and cost-efficient ML solutions. If you thrive at the intersection of deep technical expertise, system design, and cross-functional collaboration, this role is for you.

How you'll make an impact:

ML System Architecture & Production Deployment

• Design, build, and maintain end-to-end ML pipelines for batch processing of satellite and aerial imagery
• Deploy and scale ML models in production on AWS infrastructure, leveraging services like SageMaker, Bedrock,or custom-built solutions
• Implement MLflow for experiment tracking, model versioning, and model registry management
• Architect batch inference systems optimized for throughput and cost-efficiency
• Work with geospatial data formats and coordinate reference systems
• Collaborate with data scientists to transition models from research to production
• Partner with platform engineering to build scalable compute, GPU clusters, and storage layers

ML Operations & Reliability

• Implement comprehensive model monitoring systems to track performance degradation and data drift
• Design and execute canary deployments and A/B testing frameworks for safe model rollouts
• Build active learning pipelines to continuously improve model performance with minimal labeling effort
• Establish model evaluation frameworks and performance benchmarking processesCreate alerting and observability systems for production ML workloads

Technical Leadership

• Mentor ML engineers and data scientists on best practices for production ML
• Drive technical decision-making on ML infrastructure and tooling
• Collaborate with platform and data engineering teams to optimize the ML stack
• Establish engineering standards and contribute to architectural roadmaps

What we’re looking for:

• 5+ years of experience in machine learning engineering with 2+ years in a senior or lead capacity
• Proven track record deploying and maintaining ML systems in production using AWS services (SageMaker,Lambda, ECS/EKS, S3, etc.)
• Strong hands-on experience with tools like MLflow, WandB, or similar for experiment tracking and model management
• Deep expertise in image segmentation and computer vision techniques using frameworks like PyTorch or TensorFlow
• Production experience with ensemble models (xgboost, lightgbm, RF)

ML Operations Expertise

• Experience implementing model monitoring, drift detection, and alerting systems
• Hands-on experience with canary deployments, A/B testing and Shadow deployments for ML models
• Knowledge of active learning strategies and human-in-the-loop ML systems
• Strong understanding of model evaluation metrics, bias detection, and performance analysis

Technical Skills

• Expert-level Python programming with ML libraries (scikit-learn, PyTorch/TensorFlow, NumPy, pandas, etc)
• Experience with distributed batch processing frameworks (Airflow, Step Functions, Argo Workflows, Spark,Dask, Ray or similar)
• Proficiency with AWS ML ecosystem and infrastructure-as-code (Terraform, CloudFormation)
• Hands-on experience with dataset versioning tools such as DVC, LakeFS, Delta Lake, Quilt, or Pachyderm
• Strong software engineering fundamentals: unit/integration testing, CI/CD, version control, observability, designpatterns
• Experience with containerization (Docker, Kubernetes) for model deployment
• Good to have experience with ML Orchestration tools like Kubeflow, Vertex AI, etc
• Nice to have experience with GPUs: scheduling GPU jobs, optimizing GPU performance, memory profiling

We are proud to be an equal-opportunity employer. We are committed to embracing diversity and inclusion in our hiring practices, and we promote a work environment where everyone, from any race, color, religion, sex, sexual orientation, gender identity, or national origin, can do their best work.

The Role

As a SDE - 3 Platform Engineer at AiDASH, you’ll design, develop, and deploy scalable distributed systems that power our AI-driven platforms. You will build robust backend services, automated data pipelines, and analytical tools to deliver actionable insights for business-critical decisions. Partnering with product, data, and design teams, you’ll own solutions end-to-end — from system design to production deployment. Your expertise in microservices, Python, SQL/NoSQL, and cloud-native technologies (AWS, Docker, Kubernetes) will help evolve our architecture as we scale. Beyond coding, you’ll drive engineering excellence through code reviews, refactoring, and performance optimization, while mentoring peers and fostering reusable, reliable system design. If you thrive in solving complex challenges, love automation, and want to shape systems that make industries climate-resilient, this role is for you.

How you'll make an impact: