About the role
About the RoleWe are looking for a Lead AI Engineer with a strong software engineering background using Python/Java/NodeJS and hands-on experience building production-grade AI applications. The ideal candidate has spent the last 3–4 years designing and deploying GenAI solutions using LLMs, Retrieval-Augmented Generation (RAG), and AI agent frameworks.You will lead the architecture and development of AI-powered products, working closely with Product, Engineering, and Data teams to deliver scalable, enterprise-ready AI solutions.
Key Responsibilities
- Design, build, and deploy end-to-end AI applications powered by commercial and open-source LLMs.
- Develop scalable Retrieval-Augmented Generation (RAG) pipelines using enterprise data sources and vector databases.
- Design and implement AI agents and multi-agent workflows for business automation and intelligent decision-making.
- Integrate AI capabilities with backend services, APIs, databases, and enterprise applications.
- Build scalable Python services and APIs to serve AI workloads.
- Evaluate and optimize AI applications for quality, latency, reliability, and cost.
- Deploy, monitor, and continuously improve AI applications in production.
- Mentor engineers and contribute to AI architecture, engineering standards, and technical best practices.
Required Skills
Software Engineering
- 8–10 years of overall experience in software development.
- Strong understanding of software architecture, distributed systems, API design, and engineering best practices.
- Experience building production services using FastAPI or similar frameworks.
Generative AI
- 3–4 years of experience building production GenAI or LLM-based applications.
- Experience with commercial and/or open-source LLMs such as OpenAI, Claude, Gemini, Llama, or Mistral.
- Strong hands-on experience building RAG applications.
- Experience with vector databases such as Pinecone, Weaviate, pgvector, Chroma, or FAISS.
- Experience designing and implementing agentic AI systems, including orchestrator agents, multi-agent architectures, and agent-to-agent collaboration using frameworks and protocols such as LangGraph, OpenAI Agents SDK, Gemini ADK, Pydantic AI, and A2A Protocols.
- Strong understanding of prompt engineering, structured outputs, tool/function calling, and AI workflows.
Cloud & Infrastructure
- Experience with AWS, Azure, or GCP.
- Docker, Kubernetes, and CI/CD for AI applications.
- Experience deploying and operating AI applications in production.
DatabasesPostgreSQL, MySQL, MongoDB, or DynamoDB.
Nice to Have
- Experience fine-tuning open-source LLMs using LoRA, QLoRA, PEFT, or the Hugging Face ecosystem.
- Experience deploying and managing AI agents at enterprise scale.
- Hands-on experience with Amazon Bedrock, Google Vertex AI / Gemini Enterprise, Azure AI Foundry, or similar enterprise AI platforms.
- Experience with AI evaluation and observability frameworks such as RAGAS, DeepEval, TruLens, LangSmith, or similar.
- Familiarity with PyTorch, TensorFlow, or custom model development.
- Experience building AI solutions for enterprise or regulated industries.