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.