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

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

Location: Noida, India (work from office)

About the Role

We're looking for a Senior AI Developer to own the architecture of 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.

As the senior technical owner of these systems, you won't just build features — you'll set the architecture, the evaluation standards, and the production-readiness bar that the rest of the team builds against. You'll make judgment calls with full accountability for their consequences at enterprise scale: security, compliance, multi-tenant data, cost, and reliability. The systems you design go in front of live enterprise customers and directly influence real decisions about people's careers, which is exactly why this role requires someone who has already carried that kind of accountability elsewhere.

What You'll Do

• Own the end-to-end architecture of agentic and RAG systems on Azure — retrieval pipelines, agent workflows, prompt systems, and the APIs that serve them — and set the technical direction other engineers build against.

• Define and enforce evaluation standards for AI features across the team: what "good" looks like, how it's measured, and when something is genuinely ready to ship, not just working in a demo.

• Develop and oversee skills inference and proficiency models that turn evidence into skill scores enterprise customers can trust, including how that trust is established and defended under scrutiny.

• Set data engineering standards in Microsoft Fabric, including how customer data from HR, learning, and job systems is sourced, cleaned, and governed so downstream AI pipelines can rely on it.

• Make production tradeoffs on latency, cost, and reliability, and own the MLOps loop: versioning, monitoring, and retraining, at a standard other engineers are expected to follow.

• Deploy on Azure with clean APIs, containers, and CI/CD as a baseline, and make the architecture calls (build vs. buy, framework vs. custom orchestration) that less senior engineers shouldn't be making alone.

• Mentor and review the work of other engineers on the team, raising the rigor of evaluation discipline, responsible AI practice, and production readiness across the board.

• Work daily with product and engineering peers to shape what gets built, explain tradeoffs clearly to both technical and non-technical stakeholders, and document decisions that others will rely on.

• Practice responsible AI as a first-class engineering discipline: fairness, transparency, and explainability are requirements you're accountable for defending, not just implementing.

What We're Looking For

• A substantial engineering career — typically 6–8+ years in software, ML, or AI engineering — that demonstrates independent judgment and the ability to own ambiguous, high-stakes problems without close supervision.

• Deep, hands-on, enterprise-grade experience architecting agentic systems: RAG, tool calling, multi-agent orchestration, and the vector search and retrieval infrastructure behind them, built and operated under real enterprise constraints (security, compliance, multi-tenant data, SLAs) — not personal or academic projects alone.

• Strong Python and the modern AI stack: fluent with PyTorch or TensorFlow, Hugging Face, scikit-learn, Pandas, and NumPy, at a level where you're reviewing others' code, not just writing your own.

• Direct experience with the Microsoft AI stack (Azure AI Foundry, Azure OpenAI, Microsoft Agent Framework, Microsoft Fabric) is a real advantage. Deep, verifiable experience with equivalent enterprise platforms (e.g., AWS Bedrock, GCP Vertex AI) plus a credible plan for closing the Microsoft-stack gap quickly is also acceptable.

• An evaluation mindset you can install in a team, not just apply to your own work: you've built evaluation harnesses, defined metrics, and made the case — with data — for what should and shouldn't ship.

• MLOps and deployment fluency: Docker, Kubernetes, CI/CD, and operating models on Azure at a standard you'd hold a team to.

• Software engineering fundamentals strong enough to mentor from: Git, testing, API design, and the patience to debug problems in large, messy, real-world datasets.

• An AI-native way of working: you use AI coding tools daily as a core part of your craft, and you can teach others how to use them well without losing rigor.

• Clear, senior-level communication: you can explain technical tradeoffs to executives and non-technical stakeholders, not just to other engineers, and you can defend a technical decision under pushback.

• A track record of owning a production AI or ML system over time, not just shipping one: real accountability for its reliability, its failures, and its evolution in front of real users, ideally at enterprise scale.

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 other domains where model output directly affects decisions about people.

• Experience mentoring, leading, or setting technical standards for other engineers.

• Experience operating AI systems under a regulatory or compliance framework (e.g., HIPAA, SOC 2, GDPR) at enterprise scale.

What You'll Gain

• Ownership of the architecture direction for the AI core of a product already in the hands of enterprise users.

• A frontier tech stack and an AI-native team with the freedom to adopt the best tools, and the seniority to decide what those tools should be.

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

• A clear path toward staff or principal AI engineering as the team scales, with direct influence over how that team is built.

If you've architected an AI system that carried real enterprise accountability, not just shipped one; if you evaluate rigorously and can defend that rigor to a team, not just apply it to your own work; if you use AI tools as a core part of how you build, and can teach others to do the same without losing discipline; and if you're based in Noida and able to work onsite full-time, we want to hear from you.