AI Engagement Lead

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.