Lead AI Engineer

We are looking for a Lead AI Engineer to drive the design, development, and deployment of next-generation AI systems. This is a hands-on leadership role for someone who has moved beyond experimentation and has shipped production-grade AI products at scale with deep expertise across GenAI, Agentic AI, LLMs, and RAG pipelines. You will own technical architecture, guide a team of engineers, and shape how AI is built and deployed across the organisation.
Responsibilities:
Lead the end-to-end architecture and development of AI and GenAI-powered products and platforms.
Design and build agentic AI systems capable of autonomous, multi-step reasoning, planning, and task execution.
Architect and optimise RAG (Retrieval-Augmented Generation) pipelines for accuracy, latency, and scale.
Fine-tune, evaluate, and deploy LLMs for real-world, production use cases.
Build robust, scalable Python backend systems and APIs to power AI applications.
Define and drive technical roadmaps in collaboration with product, data science, and engineering leadership.
Establish best practices for AI system design, evaluation, monitoring, and responsible deployment.
Mentor and guide engineers, lead code/design reviews, and raise the technical bar across the team.
Stay current with the fast-evolving AI/GenAI landscape and evaluate new tools, models, and frameworks for adoption.
Requirements:
7-10 years of overall software engineering experience, with strong hands-on exposure to AI, GenAI, and Agentic AI system design.
Proven expertise working with Large Language Models (LLMs) including fine-tuning, prompt engineering, evaluation, and production deployment.
Practical experience building and optimising RAG pipelines, including vector databases, embeddings, and retrieval strategies.
Strong Python backend development skills, with experience designing and scaling APIs and distributed services.
Solid understanding of ML/AI fundamentals, model lifecycle management, and the practical challenges of production AI systems.
Experience with cloud platforms (AWS/GCP/Azure) and MLOps/LLMOps practices is a strong plus.
Demonstrated ability to lead technical discussions, make architectural decisions, and drive projects independently.
Strong problem-solving skills with a track record of building systems that are reliable, scalable, and maintainable.
Good to Have:
Experience with agent orchestration frameworks (e. g., LangChain, LangGraph, LlamaIndex, or equivalent).
Familiarity with model evaluation frameworks and AI observability/monitoring tools.
Prior experience mentoring engineers or leading a small technical team.
Contributions to open-source AI/ML projects or published technical work.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence/Machine Learning, or a related field (premier institute preferred).

Experience
7-10 yrs