MongoDB, as a database software and consistently voted the #1 most wanted database preferred by developers, is continuing to attract the most demanding, innovative companies and development teams in enterprise software.

To meet this demand, we are looking for passionate DevOps professionals to join our growing Industry Solutions Team (IST), a remote/flexible, globally-dispersed team. As part of our strategic investment in demo infrastructure capabilities, this role is critical to maintaining, scaling, and optimizing our demo portfolio, enabling our Industry Solutions Consultants to focus on customer engagement, content creation, and industry expertise while ensuring world-class infrastructure reliability and security. Our objective is to align MongoDB as an industry solution and ensure we understand enterprise clients and their needs to modernize software legacies, transition to modernized solutions, and guide them through continuous data-driven modernization across various industries.

MongoDB Industry Solutions Consultants communicate the direction of MongoDB's platform to customers and industry partner communities through webinars, client presentations, blog content, video content, live/virtual events, and demos. Your role will directly enable these activities by ensuring robust, reliable infrastructure that supports seamless demonstrations and proof-of-concepts across various industry verticals, helping to showcase MongoDB's capabilities to enterprise clients worldwide.

You'll work with cutting-edge technologies including Kubernetes, multi-cloud environments (AWS/GCP), and MongoDB's complete product suite, while building the infrastructure foundation that powers MongoDB's industry solution demonstrations globally. You'll collaborate closely with the global IST technical team and product teams, playing a key role in supporting deals and driving the business forward through reliable technical infrastructure.

What You Will Do at MongoDB

Infrastructure Management & Optimization

• Design and implement infrastructure-as-code templates for consistent demo deployments across 65+ demonstrations
• Optimize and maintain our KANOPY Kubernetes platform with production-grade configurations
• Manage MongoDB clusters with automated scaling, standardized security configurations, and centralized monitoring
• Maintain mixed AWS/GCP deployment environments with focus on performance and cost optimization

Security & Compliance

• Implement comprehensive security scanning and automated vulnerability remediation
• Maintain Wiz vulnerability management and security compliance across all demo environments
• Establish and enforce enterprise-grade security standards and practices
• Monitor and respond to security incidents with systematic surveillance and optimization

Automation & DevOps Excellence

• Build and maintain CI/CD automation pipelines for seamless demo deployments
• Implement autonomous end-to-end testing frameworks across the entire demo portfolio
• Deploy centralized monitoring, logging, and alerting systems for proactive issue resolution
• Create self-service developer tools to enable rapid demo development and deployment

Operational Support

• Provide dedicated incident response and system monitoring to prevent disruptions to Industry Solutions Demos
• Establish unified data model frameworks for consistent demo experiences
• Support 20-25 new demo builds annually, including complex microservices architectures
• Collaborate with wider technical team on infrastructure requirements and optimization opportunities

As an Ideal Candidate, You Will Have

Required Experience

• 5+ years of DevOps/Infrastructure engineering experience in enterprise environments
• Strong expertise in Kubernetes orchestration and container management
• Hands-on experience with Infrastructure-as-Code tools (Terraform, CloudFormation, or similar)
• Proficiency in CI/CD pipeline design and implementation (Jenkins, GitLab CI, GitHub Actions, etc.)
• Experience with multi-cloud environments (AWS, Azure, GCP) and cloud-native architectures
• Strong background in monitoring and observability tools (Prometheus, Grafana, ELK stack, etc.)

Technical Skills

• Database administration experience, preferably with MongoDB or other NoSQL databases
• Security scanning and vulnerability management experience
• Scripting and automation skills (Python, Bash, Go, or similar)
• Experience with microservices architecture and service mesh technologies
• Understanding of networking, security principles, and compliance frameworks

Soft Skills

• Excellent problem-solving and troubleshooting abilities
• Strong communication skills for cross-team collaboration
• Self-motivated with ability to work independently in a remote-first environment
• Passion for learning new technologies and staying current with DevOps trends
• Experience mentoring junior team members and sharing best practices

As an Ideal Candidate, You May Also Have

• Master's degree in Computer Science, Engineering, or equivalent work experience
• MongoDB certification or experience with MongoDB Atlas
• Experience in SaaS or enterprise software environments
• Experience with service mesh technologies (Istio, Linkerd)
• Knowledge of data pipeline and ETL processes

What You Will Learn

Technical Growth

• Deep expertise in MongoDB's complete product suite and ecosystem
• Advanced Kubernetes and cloud-native architecture patterns
• Enterprise-scale infrastructure design and security best practices
• Modern DevOps methodologies and automation frameworks

Business Context

• How infrastructure enables customer success and business growth
• Industry-specific use cases and solution architectures across various verticals

Location & Work Arrangement

This role is based in India with flexible remote work options. The Industry SolutionsTeam operates globally with collaboration across time zones. Occasional travel may be required for team events, training, or MongoDB conferences.

Why Join Our Team

• Impact: Build the infrastructure foundation that powers MongoDB's industry solution strategy
• Growth: Join a global team with significant investment and leadership support
• Innovation: Work with cutting-edge technologies in a fast-paced, evolving environment
• Culture: Be part of MongoDB's collaborative, inclusive, and learning-focused culture

This role is part of MongoDB's strategic investment in demo infrastructure capabilities, offering the opportunity to build something new while working with industry-leading technologies and a world-class team.

About MongoDB

MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform, the most widely available, globally distributed database on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.

With offices worldwide and over 60,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.

Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB. 

To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!

MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.

MongoDB is an equal opportunities employer.

Req ID: 1273401871

Job Title: Full stack developer - Bangalore and Gurgaon

About Us

“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the  British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery. 

WHY JOIN CAPCO?

You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.

MAKE AN IMPACT

Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.

#BEYOURSELFATWORK

Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.

CAREER ADVANCEMENT

With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.

DIVERSITY & INCLUSION

We believe that diversity of people and perspective gives us a competitive advantage.

Location - Bangalore, Pune, Gurugram

Experience - 5 to 8 Years

Skills and Qualifications:

About the Job: Mobile Application & Web Developer Detail-oriented and innovative Mobile Application & Light Web Developer with extensive experience building, optimizing, and supporting cross-platform native and hybrid apps and light web solutions. Proven ability to leverage modern frameworks for intuitive user experiences, integrate with critical APIs and backend services, and collaborate within agile teams. Passionate about delivering secure, customer-focused mobile and web experiences. Skills & Technologies

• Mobile: React, Angular, Ionic, Capacitor, Cordova, Typescript/JavaScript • (Swift (iOS), Kotlin (Android)) • Web: React, Angular, TypeScript, JavaScript (ES6+), Material UI, Redux • Testing & CI/CD: Jest, Jasmine, Karma, CodeMagic, AppFlow, GitLab Pipeline • Cloud & APIs: Firebase, AWS, RESTful APIs, GraphQL, MongoDB, SQL, Redis • Other: CSS3, SASS, HTML5, PWA, Offline-first/Local Storage, UI/UX Design • Workflow: Agile/Scrum, Remote collaboration, Unit testing, Code reviews

Technical Proficiencies Category || Technologies / Tools Mobile Dev || React, Ionic, Capacitor, Cordova, Swift, Kotlin Web Dev || React, Angular, TypeScript, UI Libraries (Material, Tailwind) APIs/Cloud || RESTful APIs, GraphQL, Firebase, AWS Testing/CI || Jest, Jasmine, GitHub Actions

If you are keen to join us, you will be part of an organization that values your contributions, recognizes your potential, and provides ample opportunities for growth. For more information, visit www.capco.com. Follow us on Twitter, Facebook, LinkedIn, and YouTube.

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.

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.

Job Description

 PayPay is looking for a Frontend Engineer to build and scale UI/Product experiences for our web applications. You’ll work closely with Product, Design, and Backend teams to deliver high-quality features, contribute to a growing design system.

Primary Responsibilities

• Deliver product features end-to-end: translate requirements into clean, scalable UI implementations with well-structured components and predictable data flow.
• Build reusable UI components and patterns: contribute to and extend our design system using tools like Storybook; prioritize consistency, accessibility basics, and developer ergonomics.
• Integrate APIs and handle real-world states: implement robust data fetching patterns (pagination, caching, retries), and manage loading/error/empty states with clear UX behavior.
• Write maintainable, modular code: apply good separation of concerns (presentation vs business logic vs API layer), meaningful abstractions, and clear naming; keep codebase easy to extend.
• Optimize performance and reliability: identify bottlenecks (rendering, network, bundle size), apply pragmatic improvements (memoization, debouncing, virtualization where relevant), and avoid async pitfalls (stale responses, race conditions).
• Improve engineering quality: participate actively in code reviews, raise the bar on standards, and help enforce best practices around testing strategy, i18n, and build tooling.
• Collaborate cross-functionally: work closely with PM/Design/Backend to clarify requirements, propose tradeoffs, and deliver iteratively with high ownership.
• Support operational excellence: use observability tooling (e.g., Sentry, New Relic, Kibana) to debug production issues, measure impact, and improve stability.
• Grow toward full-stack ownership (optional path): partner with backend teams and gradually take on BFF/API contributions using Node.js (NestJS/Express) as needed.

Tech Stack

Frontend: Typescript/Javascript , React/Vue, single-spa, Webpack, Jest, Storybook, i18n , micro-frontend

Middlewear : NestJS, Express

Infra/CI: AWS, Docker, Kubernetes, ArgoCD, Terraform, GitHub Actions, Jenkins

Obs/Analytics: Sentry, Kibana, New Relic, Google Analytics, Firebase

Qualifications

• 3 - 5 years of professional frontend development experience.
• Strong JavaScript fundamentals.
• Solid experience in React and/or Vue.
• Proven ability to write readable, modular, production-quality code.
• Strong problem-solving skills with practical DSA (web-relevant) comfortable reasoning about time/space complexity.
• Performance awareness: can identify bottlenecks and implement pragmatic optimizations.
• Ability to do low-level design (LLD) for frontend features: break down requirements into components/modules, define interfaces, and plan implementation in milestones.
• Good communication: can explain approach and tradeoffs clearly.
• Leverage AI/LLM based tools in daily workflows

Preferred Qualifications

• Experience with Nest.js (routing, SSR/CSR tradeoffs, data fetching strategies).
• Experience with Node.js or API integration patterns (REST, pagination, auth flows, error handling).
• Familiarity with design systems, accessibility basics, and responsive UI patterns.
• Exposure to frontend observability (error logging, basic performance metrics).
• Experience with micro-frontends (e.g., single-spa) or large-scale frontend architectures.

What success looks like

• You can independently deliver a feature end-to-end with clean architecture and reliable async handling.
• Your code is modular, maintainable, and performance-conscious.
• You contribute to consistent UI patterns and raise team quality through reviews and collaboration.

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.
　　　

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.