AI Engineering Lead

Location: India

Employment Type: Full-Time

Required skills:

• 12+ years of professional experience as a software engineer and building applications/systems.

• 2+ years of hands-on experience in how LLMs work & Generative AI (LLM) techniques, particularly multi-agent systems.

• Expert proficiency in programming skills in Python, Langgraph, and SQL is a must.

• Expert in architecting GenAI applications/systems using various frameworks & cloud services.

• Expert proficiency in using AI tools like Claude Code, Codex, cursor, windsurf, and the like.

• Expert proficiency in AI observability & evaluation tools like Langsmith, Langfuse, or similar.

• Good proficiency in using various cloud services from Azure, GCP, or AWS for building GenAI applications.

• Experience in driving the engineering team toward a technical roadmap.

• Excellent communication skills to effectively collaborate with business SMEs.

Roles & Responsibilities:

Solutioning & Lead

• Build the technical roadmap given a business requirement and own the delivery of the same.

• Lead the engineering team toward a technical roadmap and ensure the timely execution of the roadmap to achieve customer satisfaction.

• Design robust multi-agent architectures, including supervisor-router patterns with dynamic sub-agent routing and stopping conditions.

• Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.

Hands-on skills

• Develop LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like retrieval-augmented generation (RAG) and multi-agent-based architectures.

• Build and maintain agent evaluation pipelines, including offline eval datasets, LLM-as-judge, and CI-integrated eval runs.

• Codebase ownership: Build & maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.

• Cloud integration: Deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.

Communication & Cross-functional collaboration

• Actively follows the frontier and has differentiated, up-to-date views on model releases, agentic architectures, evaluation methods, tool-use and computer-use patterns, multimodal capability, reasoning/test-time compute trends, and the serious open questions in the field.

• Produce a structured, high-signal answer to an open-ended technical or strategic question — while modulating depth for a non-engineering executive audience.

• Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.