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.

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.

Cross-functional collaboration: Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.