Lead AI Engineer

Experience : 9+ Years

Location : Bangalore & Noida

Employment Type : Full-Time

Role Overview :

We are looking for a Lead AI Engineer with strong hands-on experience in Generative AI, Agentic AI, Python, and Machine Learning to lead the design and development of intelligent AI-powered solutions.

The ideal candidate should have experience building and deploying LLM-based applications, AI agents, RAG pipelines, and machine learning solutions in enterprise environments.

The candidate will work closely with product, engineering, and business teams to translate business problems into scalable AI solutions.

Primary Skills :

- Generative AI / Agentic AI

- Python

- Machine Learning

Key Responsibilities :

- Lead the architecture, development, and deployment of Generative AI and Agentic AI solutions.

- Design and build LLM-powered applications, AI agents, copilots, and intelligent automation solutions.

- Develop scalable RAG architectures using enterprise data sources and vector databases.

- Build AI agents capable of reasoning, planning, tool usage, decision-making, and task execution.

- Develop and integrate LLMs from providers such as OpenAI, Azure OpenAI, Anthropic, Google Gemini, or open-source models.

- Design and implement prompt engineering, structured outputs, function/tool calling, and agent workflows.

- Apply Machine Learning and NLP techniques to solve complex business problems.

- Develop Python-based AI/ML applications and APIs.

- Work with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or similar.

- Implement model evaluation, monitoring, guardrails, hallucination reduction, and responsible AI practices.

- Collaborate with Data Scientists, ML Engineers, Software Engineers, Architects, and business stakeholders.

- Lead technical discussions, conduct code/design reviews, and mentor junior AI/ML engineers.

- Optimize AI solutions for performance, scalability, reliability, cost, and latency.

- Support production deployment of AI/ML solutions using cloud and DevOps/MLOps practices.