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.

 Transparent 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

Pay: ₹3,000,000.00 - ₹3,500,000.00 per year

Work Location: In person

📌 AI Engineering Lead (India)
🏢 Acme Services
📍 India