AI Engagement Lead (Agentic AI)

About the role:

Client is looking for people with GenAI experience to join us in solving business problems for our Fortune 500 customers. You will be a key member of the Turing Intelligence delivery organizations and part of a GenAI project. You will be required to lead a team of other Turing engineers across different skill sets. In the past, the Turing GenAI delivery organization has implemented industry leading multi-agent LLM systems and LLM deployments for major enterprises.

Required skills:

• 12+ years of professional experience in software engineers 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 likes.
• Expert proficiency in AI observability & evaluation tools like Langsmith, Langfuse or similar
• Good proficiency in using various cloud services from Azure, Google Cloud Platform, or AWS for building the 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 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, Google Cloud Platform, or AWS), optimizing resource usage and ensuring robust CI/CD processes.