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.

Role Summary

We are looking for a technically strong AI / ML Developer with hands-on expertise in training and fine-tuning Small Language Models (SLMs) and RAG Solutions. The ideal candidate will drive end-to-end AI Solutions — from dataset curation and pre-processing to training, evaluation, and production deployment. You will collaborate closely with product, product engineers, and infrastructure teams to build AI solutions that are efficient, scalable, and business-aligned.

Key Responsibilities

1  Model Design & Training

• Design, train, and fine-tune Small Language Models (SLMs) using frameworks such as PyTorch, TensorFlow, or JAX.
• Conduct experiments with supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuning.
• Implement efficient training pipelines leveraging distributed training (DDP, FSDP) across GPU/TPU clusters.
• Perform hyperparameter optimisation, ablation studies, and model selection based on benchmark results.
• Develop and maintain data pipelines for collecting, cleaning, tokenising, and pre-processing large-scale training corpora.

2  Model Evaluation & Quality

• Define and implement evaluation frameworks including perplexity, BLEU, ROUGE, BERTScore, and task-specific benchmarks.
• Conduct red-teaming, bias analysis, and safety evaluations to ensure responsible AI deployment.
• Benchmark models against established baselines (e.g., GPT-2, Phi, Mistral) and track performance over iterations.
• Collaborate with QA teams to build regression suites for model versioning and continuous evaluation.

3  MLOps & Deployment

• Containerise and deploy models using Docker, Kubernetes, and cloud-native ML platforms (AWS SageMaker / GCP Vertex AI / Azure ML).
• Build and maintain model registries, experiment tracking (MLflow, Comet), and reproducible training pipelines.
• Optimise inference performance through quantisation (INT8, INT4), pruning, distillation, and ONNX/TensorRT conversion.
• Monitor model drift, data drift, and performance degradation in production; implement automated retraining triggers.

4  Research & Innovation

• Stay current with state-of-the-art NLP/LLM research; prototype and validate new techniques from published literature.
• Contribute to internal knowledge sharing, technical documentation, and model cards.
• Explore parameter-efficient fine-tuning (PEFT) methods such as LoRA, QLoRA, and Adapter layers.
• Investigate and apply mixture-of-experts (MoE), retrieval-augmented generation (RAG), and agentic workflows as needed.

5  Collaboration & Stakeholder Management

• Work with product managers and domain experts to translate business requirements into model objectives and KPIs.
• Communicate model capabilities, limitations, and trade-offs clearly to both technical and non-technical stakeholders.
• Participate in architecture reviews, sprint planning, and cross-functional design discussions.

Required Qualifications

1  Education

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related quantitative field.
• Equivalent professional experience with a strong portfolio of delivered AI/ML projects will be considered.

2  Experience

• 4 – 6 years of professional experience in machine learning or NLP engineering.
• Minimum 2 years of direct, hands-on experience in training or fine-tuning language models (LLMs or SLMs).
• Demonstrable experience taking a model from dataset preparation through to production deployment.

3  Technical Skills — Core

• Programming: Python (proficient); familiarity with C++/CUDA a plus.
• Deep Learning Frameworks: PyTorch (primary), TensorFlow or JAX.
• NLP Libraries: Hugging Face Transformers, Datasets, PEFT, TRL, Accelerate.
• Training Infrastructure: Multi-GPU training, FSDP, DeepSpeed ZeRO stages 1–3.
• Data Engineering: Pandas, NumPy, Apache Spark or Dask for large-scale data prep.
• Vector DBs & Retrieval: FAISS, Pinecone, Weaviate, or equivalent.
• Cloud Platforms: AWS, GCP, or Azure — ML-specific services (SageMaker, Vertex AI, etc.).
• Version Control & CI/CD: Git, GitHub/GitLab, MLflow or W&B for experiment tracking.

4  Technical Skills — Nice to Have

• Experience with multimodal models or vision-language models (VLMs).
• Knowledge of model safety, alignment techniques, and responsible AI frameworks.
• Familiarity with LangChain, LlamaIndex, or similar orchestration frameworks.
• Contributions to open-source ML projects or publications at NeurIPS, ICML, EMNLP, ACL.

Behavioural Competencies

• Intellectual Curiosity: Keeps up with rapidly evolving AI research and applies learnings pragmatically.
• Problem Solving: Approaches ambiguous problems with structured, data-driven thinking.
• Ownership: Takes full accountability for model quality, timelines, and production reliability.
• Collaboration: Works effectively in cross-functional teams with diverse skill sets.
• Communication: Distils complex technical concepts for diverse audiences.
• Adaptability: Thrives in a fast-paced environment where priorities evolve with research breakthroughs.

Role Summary

We’re looking for a Principal Architect who is a tech enthusiast with a drive for excellence and an out-of-the-box mindset to satisfy business needs in a complex payments environment. You will be a key technical leader in our Cloud Modernization initiative while also accelerating how we use AI to improve engineering velocity, reliability, and customer outcomes by enabling safe, governed adoption of GenAI, building agentic systems, and embedding AI-assisted capabilities into our platforms and workflows.

What You’ll Do

· You will introduce AI-enabled capabilities to solve engineering and business problems by identifying high-value use cases, enabling AI adoption and AI-led development practices across teams, and delivering measurable outcomes.

· You will build and ship agentic systems that can plan, execute, and collaborate with humans safely with robust controls, and help build core AI capabilities to scale adoption.

Who You Are

· You also have a strong point of view on practical AI adoption. You can translate real engineering pain points into AI-enabled solutions, select the right patterns and ship systems with strong evaluation, observability, and governance.

Key Skills and Competencies

· Experience in designing and shipping LLM-enabled applications and agentic architectures in production environments, with sound judgment on when not to use agents.

· Experience in responsible AI governance, evaluation, and data safety, including prompt‑injection mitigation, data access controls, secrets handling, policy enforcement, audit logging, offline/online evaluation, observability, red‑teaming, cost/latency monitoring, and safe handling of enterprise and PII data.

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.

Location Preference: National Capital Region, New Delhi

This is an in office position 5 days a week.

About Us:

Sauce Labs is the world’s largest full-lifecycle, test automation platform, and the company behind Selenium. Trusted by 80% of the world’s top ten largest financial institutions and over 300,000 enterprise users, Sauce Labs provides the only AI platform capable of turning business intent into autonomous testing and quality assurance. With a proprietary dataset of 8.7 billion test runs, Sauce Labs empowers the Fortune 2000 to bridge the gap between AI-driven code generation and enterprise-grade software quality. Learn more at saucelabs.com.

The Role:

BuildIQ is seeking a Senior NodeJS Engineer with expert-level TypeScript skills to build and scale modern, high-performance services. You will be responsible for the development of Visual and AI for Authoring, ensuring it meets the highest standards and architectural integrity. In this role, you will bridge complex backend logic with seamless performance to drive the next generation of our product ecosystem.

Responsibilities: 

• Maintain applications written in high-level programming languages such as NodeJS in TypeScript.
• Strive for constant improvement and contribute to the power of our agile development culture
• Collaborate with other teams on building our platform
• Participate in on-call rotations to help teams troubleshoot urgent customer-facing problems and other engineering issues

Required Skills:

• Good Experience with TypeScript (NodeJS)
• Experience with CI/CD and Docker
• Experience with relational or non-relational databases
• Experience with observability and monitoring
  
  

Nice to Have:

• Experience with Rust or Go 
• Experience with other high level programming languages like Java or C#
• Experience working in both high volatility (startup) and bigger organization environments
• LLM Agent Development: LangChain/similar frameworks + tool development (MCP, function calling)
• Agent Communication Protocols: MCP and/or A2A patterns
• Prompt Engineering: System prompt design, optimization, output validation
• Managed LLM Services: GCP Vertex AI, AWS Bedrock, or Azure OpenAI

Please note our privacy terms when applying for a job at Sauce Labs.

Sauce Labs is proud to be an Equal Opportunity employer and values diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender identity/expression/status, sexual orientation, age, marital status, veteran status or disability status.

Security responsibilities at Sauce

At Sauce, we will commit to supporting the health and safety of employees and properties, partnering with internal stakeholders to learn and act on ever-evolving security protocols and procedures. You’ll be expected to fully comply with all policies and procedures related to security at the department and org wide level and exercise a ‘security first’ approach to how we design, build & run our products and services.

Who we are

At Fluxon, we believe that how you build matters as much as what you build. We help businesses navigate their most important technology decisions with confidence, and take responsibility for seeing them through. Founded by ex-Googlers and startup veterans, we’re proud to partner with teams behind some of the most ambitious products, including Google, OpenAI, Anthropic, Walmart and Stripe. 

Our work spans strategy, design, and engineering — often in complex, AI-driven environments — where clarity, speed and quality are the standard. We use AI intentionally, applying it only where it adds real value and expands what’s possible. Care shapes everything we do.

Inside Fluxon, you’ll find a global, remote-first team of experienced builders, who are curious, kind and serious about their craft. We’re building a place where people can take ownership, solve problems that matter and do work they’re proud to stand behind. If you want to do your best work alongside people who care as much as you do, you’ll feel at home here.

The role is open to candidates based in Gurgaon, India.

About the role

As an AI Engineer at Fluxon, you’ll take the lead in designing, building and deploying AI-powered applications for our clients.

You'll be responsible for:

• System Architecture: Design and implement end-to-end AI systems and their parts, including data ingestion, preprocessing, model inference, and output structuring
• Generative AI Development: Build and optimize RAG (Retrieval-Augmented Generation) systems and Agentic workflows using frameworks like LangChain, LangGraph, ADK (Agent Development Kit), Genkit
• Production Engineering: Deploy models to production environments (AWS/GCP/Azure) using Docker and Kubernetes, ensuring high availability and scalability
• Evaluation & Monitoring: Implement feedback loops to evaluate model performance (accuracy, hallucinations, relevance) and set up monitoring for drift in production
• Collaboration: Work closely with other engineers to integrate AI endpoints into the core product and with product managers to define AI capabilities
• Model Optimization: Fine-tune open-source models (e.g., Llama, Mistral) for specific domain tasks and optimize them for latency and cost

You'll work with technologies including:

Languages

• Python (Preferred)
• Java / C++ / Scala / R / JavaScript   

AI / ML

• LangChain
• LangGraph
• Google ADK
• Genkit
• OpenAI API
• LLM - Large Language Model
• Vertex AI

Cloud & Infrastructure

• Platforms: Google Cloud Platform (GCP) or Amazon Web Services (AWS)
• Storage: Google Cloud Storage (GCS) or AWS S3
• Orchestration: Temporal, Kubernetes

Data Stores

• PostgreSQL
• Firestore
• MongoDB

Monitoring & Observability

• GCP Cloud Monitoring Suite

Qualifications

• 5+ years of industry experience in software engineering roles
• Strong proficiency in Python or any preferred AI programming language such as Scala, Javascript and Java
• Strong understanding of Transformer architectures, embeddings, and vector similarity search
• Experience integrating with LLM provider APIs (OpenAI, Anthropic, Google Vertex AI)
• Hands-on experience with agent workflows like LangChain, LangGraph
• Experience with Vector Databases and traditional SQL / NoSQL databases
• Familiarity with cloud platforms, preferably GCP or AWS
• Understanding of patterns like RAG (Retrieval-Augmented Generation), few-shot prompting, and Fine-Tuning
• Solid understanding of software development practices including version control (Git) and CI/CD

Nice to have:

• Experience with Google Cloud Platform (GCP) services, specifically Vertex AI, Firestore, and Cloud Functions
• Knowledge of prompt engineering techniques (Chain-of-Thought, ReAct, Tree of Thoughts)
• Experience building "Agentic" workflows where AI can execute tools or API calls autonomously

What we offer

• Remote-first, flexible work with a budget to set up a work space that works for you
• Localized healthcare coverage to support you and your family’s wellbeing
• Flexible paid time off with a minimum of 3 weeks per year (plus holidays)
• "No internal meetings" Fridays, so you can focus on deep, uninterrupted work
• A professional growth budget for learning that matters to you –  whether it’s developing your technical skills or learning a new language
• A monthly wellness allowance to support your physical and mental health
• Annual company offsites, where we gather in person to build meaningful connections
• A competitive salary that reflects your expertise, impact and experience
• Profit-sharing, so you can benefit directly from the value we build together
• A paid sabbatical program, designed for rest, renewal and fresh perspective

About Nanonets

Nanonets has a vision to help computers see the world starting with reading and understanding documents. Our product helps businesses automate document related workflows for enterprise office teams such as invoice data entry for AP teams, KYC automation for banks and insurance etc. Some of the companies we work with include Toyota, Boston Scientific, Bill.com and Entergy to name a few. 

We recently raised a series B round of $29 million. Now 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. 

Job description

At Nanonets, we are building AI-powered automation agents to eliminate manual healthcare admin tasks - helping customers streamline operations and deliver better patient care.

We are looking for a Full Stack Software Engineer who thrives in fast-paced startup environments, enjoys shipping impactful products quickly, and can work across the stack to bring our AI agents to life.

You’ll work with a small, high-performing team building production-grade AI agents - combining distributed systems, AI integration, and intuitive web platforms.

What You’ll do : 

• Design, develop, and deploy AI agent services that automate healthcare workflows.
• Build scalable APIs, backend services, and sleek frontend interfaces.
• Ship high-quality, maintainable code - fast.
• Collaborate closely with product and AI teams to iterate on agent workflows.
• Monitor, debug, and improve production systems continuously.

What we expect from you :

• 3-6 years of hands-on experience in full stack engineering.
• Experience working in a fast-moving startup, with a track record of shipping products quickly.
• Proficiency in at least one backend language (Go or Python) and one frontend framework (preferably React + TypeScript).
• Experience with using LLM APIs and LLM-based applications.
• Strong understanding of web architecture, distributed systems, APIs, and relational databases.
• Ability to write clean, testable code and solve production issues effectively.
• A builder’s mindset: ownership, initiative, and bias for action.

Nice to have :

• Familiarity with US healthcare or other regulated domains.
• Knowledge of DevOps practices, cloud infrastructure, and CI/CD pipelines.

Our Tech Stack :

• Backend: Go, Python
• Frontend: React, TypeScript
• Databases & Infra: PostgreSQL, Redis, AWS/GCP

Why join us ?

• Work on cutting-edge AI agents transforming the healthcare industry.
• Be part of a small, high-impact, no-bureaucracy team.
• Ship products that make a real-world difference for customers.
• Learn fast, grow fast - with competitive compensation & career growth opportunities.

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