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AI Developer | Onsite, Noida | Open to India-based candidates only

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AI Developer | Onsite, Noida | Open to India-based candidates only

The Details

Experience: 3+ years, with at least one AI/ML system shipped to production

Works with: Product and a small, fast-moving engineering team

Location: Noida, India (work from office)

About the Role

We're looking for an AI Developer to build the intelligence at the core of our AI-native talent and skills intelligence platform. This isn't a role where AI is a feature bolted onto a product. The product is the AI: a proficiency engine that scores skills from real evidence, retrieval and agent systems that guide learning and career decisions, and data pipelines that turn organizational data into actionable skills intelligence. You'll design, build, evaluate, and ship these systems to production, where they directly influence real enterprise users' decisions about people and careers.

You'll work in a small engineering team that ships quickly, using AI tools (including AI coding assistants) as a standard part of how we build. You'll own real systems from day one—the code you write goes in front of live customers, which is exactly why it has to be rigorous.

What You'll Do

  • Build LLM-powered features end to end on Azure: retrieval pipelines, agent workflows, prompt systems, and the APIs that serve them inside the product

  • Own evaluation for AI features—define what "good" looks like, build evaluation harnesses, measure quality systematically, and iterate until the feature earns its place in production

  • Develop skills inference and proficiency models that turn evidence (assessments, work artifacts, learning signals) into skill scores customers can trust

  • Engineer data foundations in Microsoft Fabric: source, clean, and structure customer data from HR, learning, and job systems so AI pipelines can use it

  • Take models to production: optimize for latency, cost, and reliability, and maintain the MLOps loop of versioning, monitoring, and retraining

  • Deploy on Azure with clean APIs, containers, and CI/CD as standard practice

  • Work daily with product and engineering peers to shape what gets built, explain tradeoffs clearly, and document decisions

  • Practice responsible AI: fairness, transparency, and explainability are engineering requirements, not afterthoughts

What We're Looking For

  • Strong Python and the modern AI stack. You're fluent with PyTorch or TensorFlow, Hugging Face, scikit-learn, Pandas, and NumPy. You write code other engineers want to inherit.

  • Hands-on LLM application experience. You've built real systems with RAG, agents, tool calling, and structured prompting—including the vector search and retrieval infrastructure behind them.

  • Experience with the Microsoft AI stack. The platform is built on Azure AI Foundry, Azure OpenAI, the Microsoft Agent Framework, and Microsoft Fabric. Direct experience here is a real advantage. Deep experience with equivalent tools plus appetite to go deep on ours also works.

  • An evaluation mindset. You don't call an AI feature done because the demo worked. You build evals, test systematically, and use metrics to decide what ships.

  • MLOps and deployment comfort. Docker, Kubernetes, CI/CD, and operating models on Azure are part of your toolkit.

  • Software engineering fundamentals. Git, testing, API design, and the debugging patience to work through problems in large datasets.

  • An AI-native way of working. You use AI coding tools daily as a core part of your craft, not an occasional helper.

  • Clear communication. You can explain technical tradeoffs to non-technical stakeholders in plain language, in writing and in person.

  • A track record of shipping. Years matter less than evidence—at least one AI or ML system you built that ran in production for real users.

Nice to Have

  • Experience fine-tuning or adapting open-weight models, and depth in classical ML and NLP beyond LLM APIs

  • A grounding in statistics and optimization

  • Prior work in HR technology, learning, talent, or domains where model output directly affects decisions about people

What You'll Gain

  • Ownership of the AI core of a product already in the hands of enterprise users

  • A frontier tech stack and an AI-native team with freedom to adopt the best tools

  • Real domain expertise in skills and talent, learned from live customers

  • A clear path toward senior and lead AI engineering as the team scales

If you've built an AI system live in front of real users, evaluate rigorously before shipping, use AI tools as a core part of how you code, care about getting the technical details right because real decisions about people's careers depend on it, and are based in Noida and able to work onsite full-time, we want to hear from you! Apply now.

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What this application asks

recruitee

Full name, Email, CV, Cover letter, Phone

  • This is a full-time, on-site role based in our Noida office. Remote or hybrid work is not available. Are you currently located in Noida or willing and able to work from our Noida office full time? yes / no
  • Where are you currently located?
  • What motivated you to apply for this position, and how does it fit with your career goals? written answer
  • Describe a Generative AI use case you’ve worked on that involved Retrieval-Augmented Generation (RAG), embeddings, or vector search. What problem were you solving, which tools or frameworks did you use, and how was the solution used in production or by end users? written answer
  • Which large language models have you worked with directly (for example GPT-4, Claude, LLaMA, BERT, or others)? Briefly explain how you used each model and whether you fine-tuned it, prompt-engineered it, or integrated it via an API. written answer
  • What is your experience with the Microsoft AI stack (Azure AI Foundry, Azure OpenAI, Microsoft Agent Framework, Microsoft Fabric)? If you have direct experience with these tools, describe a project where you used them. If not, which equivalent platforms and tools have you used deeply in production, and how would you approach learning the Microsoft stack? written answer
  • Describe your experience building agent systems or using tool calling with large language models. Have you worked with agent frameworks, structured prompting, or orchestration of multiple function calls? Walk us through a project: what problem were you solving, which framework or approach did you use, and what was challenging about moving it to production? written answer
  • Describe a machine learning or AI model you have deployed into a production environment. Where was it deployed (cloud or on-premise), how was it monitored, and what tools or processes did you use for deployment, versioning, and retraining? written answer
  • Provide an example of how you optimized an AI or machine learning model for performance, latency, or scalability. What constraints were you working within and what tradeoffs did you make? written answer
  • Describe your experience engineering data foundations for AI/ML systems. Have you worked with Microsoft Fabric or similar cloud data platforms? Walk us through a project where you sourced, cleaned, and structured data for AI pipelines—what types of data were involved, what decisions did you make about preprocessing and feature engineering, and how did those decisions impact model performance or downstream AI capability? written answer
  • Walk us through how you define and measure what "good" looks like for an AI or Generative AI system you've built. Describe the evaluation harness or framework you built—what metrics did you use, how did you construct evaluation sets, and what role did human review or feedback loops play? Give an example of how evaluation results changed what shipped to production. written answer
  • Describe a situation where you had to address ethical risks in an AI system, such as bias, hallucinations, or incorrect outputs. How did you or your team identify, mitigate, and communicate those risks? written answer
  • How do you use AI coding tools (such as GitHub Copilot, Claude, or similar) as part of your development workflow? Give a concrete example of how you've used AI to build better code faster, and describe how you evaluate and validate the code AI generates. written answer
  • Have you worked on machine learning or AI systems in domains where the model output directly influences decisions about people (such as HR, recruitment, learning, talent, or similar)? If yes, describe the domain and the specific challenges you faced in ensuring fairness, transparency, or explainability. If no, describe how you would approach building such a system. written answer
  • What is your current CTC (in INR)?
  • What is your expected CTC (in INR) for this role?
  • What is your current notice period?
  • Is this notice period negotiable? yes / no
  • Please feel free to share a 2-minute video to introduce yourself and why you are interested in and qualified for this role. optional

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