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.