Lead AI Engineer-8-12Yrs-NCR

Role Summary

• Development and production-scale delivery of Solutions & Platform components
• Hand-On with new age technologies-programming-tooling e.g. Context Engineering, Knowledge Graphs, Loop Engineering, and Agents Harness
• Experience of SDKs-DevSecOps Pipelines-Code Repositories
• Knowledge of Cloud & Multi-Modal Systems
• Implementation of Enterprise design principles and considerations such as Responsible & Secure AI, Observability, LLM Ops, AI Runtime, and Token Economics
• Aligned to Agentic AI Technical Architecture practices and standards

Key Responsibilities

1. Rapid Prototyping & Application Development

• Build AI applications, copilots, and agentic workflows end-to-end UI, APIs, business logic, and model integration.
• Use rapid development tools (Cursor, Claude Code, Replit, Google AI Studio) to compress build cycles and iterate quickly with users and stakeholders.
• Turn loosely-defined requirements into working demos and prototypes within days, then refine based on feedback.

2. Agentic & GenAI Engineering

• Develop with agentic SDKs and frameworks – OpenAI Agents SDK, Anthropic Claude (Agent SDK / API), Google Gemini & ADK, LangChain/LangGraph.
• Implement RAG pipelines, tool/function calling, structured outputs, and prompt engineering with systematic testing and evals.
• Integrate models and agents with enterprise data sources and APIs, handling auth, rate limits, and error paths properly.

3. Engineering Quality & Productionization

• Write clean, testable, well-documented code; use Git, containers, and CI/CD as standard practice.
• Partner with Forward Deployment Engineers and platform teams to take successful prototypes into production, adding monitoring, guardrails, and cost controls.
• Balance speed and quality pragmatically – knowing when to hack and when to harden.

4. Collaboration & Continuous Learning

• Work closely with architects, data scientists, and designers; contribute to demos, accelerators, and internal hackathons.
• Stay current with the fast-moving model and tooling landscape, and share learnings across the team.
• Evangelize AI-assisted development practices that raise the whole team’s velocity.

Technical Skills & Tooling (Hands-On)

• Rapid development tools as daily drivers: Cursor, Claude Code, Replit, Google AI Studio, GitHub Copilot – demonstrated ability to ship real software with AI-assisted workflows.
• Agentic SDKs & frameworks: hands-on experience with OpenAI Agents SDK, Anthropic Claude APIs/Agent SDK, Google Gemini/ADK, and LangChain or LangGraph.
• Strong programming skills in Python and/or TypeScript/JavaScript; comfort building full-stack prototypes (React/Node) and REST APIs.
• LLM application patterns: prompt engineering, function/tool calling, structured outputs, RAG with vector stores (pgvector, Pinecone, FAISS, or similar).
• Testing & observability basics: writing evals, using tracing tools (LangSmith, Langfuse, or similar), and monitoring cost/latency/quality.
• Engineering foundations: Git, Docker, CI/CD, and at least one cloud (AWS/Azure/GCP).
• Good to have: voice/multimodal experience (ElevenLabs, HeyGen), MCP-based tool integration, fine-tuning or open-source LLM experience.

Key Outcomes & Success Metrics

• Speed of delivery: consistent idea-to-prototype turnaround in days and prototype-to-production in weeks.
• Volume and quality of shipped work: applications, demos, and accelerators that are actually used by stakeholders and internal teams.
• Reliability of what ships: low defect rates, sensible test/eval coverage, and predictable cost/latency behavior.
• Contribution to reuse: components, patterns, and utilities adopted by other engineers.
• Team velocity uplift through shared AI-assisted development practices.

Required Experience & Qualifications

• 4–8 years of software engineering experience, with 1–2+ years building GenAI/LLM applications hands-on.
• A portfolio of shipped AI work – products, prototypes, GitHub projects, or demos you can walk us through.
• Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent practical experience).
• Demonstrated fluency with AI-native development tools (Cursor, Claude Code, Replit, AI Studio) in real projects – not just experimentation.
• Strong problem-solving skills and product sense – you care about whether the thing you built actually gets used.
• Clear written and verbal communication; comfortable demoing your work to technical and business audiences.

Behavioral Expectations

• Builder’s mindset – bias toward shipping, learning from real usage, and iterating.
• Relentless curiosity – self-driven learning in a landscape where the best tool changes every quarter.
• Pragmatic judgment on speed vs. quality trade-offs.
• Low-ego collaboration – gives and takes feedback well, helps teammates move faster.
• Responsible AI awareness – builds with security, privacy, and ethical use in mind from day one.