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

AI Developer - OneGuru | Onsite, Noida | Open to India-based candidates only The Details Experience: 3+ years, with at least one AI/ML system shipped to production Reports to: Engineering Lead 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 OneGuru—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