Associate Director- AI Engineering

The Associate Director- AI Engineering will lead AI engineering, platform development and client delivery in Bangalore, India. The role focuses on moving agentic AI, RAG and LLM applications from solution design and prototypes into secure, reusable, production-ready deployments for Trinity’s life sciences clients.

Responsibilities

• Lead architecture and end-to-end delivery of AI applications and products. Set reference architectures, engineering patterns, evaluation approaches and guardrails for agentic systems. Guide work on multi-agent architectures, MCP/A2A interoperability, agent memory, tool use, human-in-the-loop workflows, model selection, inference optimization and application observability.
• Establish coding, testing, CI/CD, security, RBAC and agent-harness standards. Remain hands-on with critical technical problems and prototypes, including work involving LangGraph, AWS Bedrock, Snowflake Cortex, Azure AI Foundry and Databricks.
• Lead the AI Platform/Foundations engineering team. Develop reusable components, self-service deployment patterns and infrastructure automation; standardize containerization, model serving, orchestration, observability and environment management. Own platform reliability, security, cost optimization and operational readiness across cloud infrastructure, Kubernetes, networking, access management and secrets.
• Address applicable client compliance needs, including data residency, access audit trails and workload isolation. Partner with centrally governed IT and cloud teams to secure appropriately sized platform capabilities.
• Lead and mentor AI and platform engineers through reviews, coaching and hands-on guidance. Manage priorities, dependencies, risks and resources across workstreams; remove delivery blockers, support hiring and capability development, and work with Data Science, Product and client-facing stakeholders on AI solutions and roadmaps. Evaluate emerging agentic AI techniques and promote useful AI-enabled development practices.

Qualifications

• At least 12 years of software or technology engineering experience, including at least 5 years in AI/ML, generative AI or AI application engineering, plus demonstrated engineering-team technical leadership.
• Deep expertise in LLM applications, RAG, agentic AI and production AI engineering; strong Python, Spark and SQL fundamentals. Experience deploying enterprise AI solutions on AWS, Azure, Databricks or Snowflake, and knowledge of Docker/Kubernetes, CI/CD, infrastructure automation, model serving, observability, security and cloud architecture. Strong stakeholder communication and a record of improving AI delivery through standards and reusable platforms are required.
• A degree in Computer Science, Engineering or a related field from an IIT, NIT, BITS or comparable institution is sought; a strong applied track record is considered equally. AWS experience is preferred. Life sciences, pharmaceutical or other regulated-enterprise experience is a strong differentiator.