Job Description

Associate Director Commercial AI Tech Lead Digital Human Health

Job Description

This role sits within the Commercial AI vertical of Digital Human Health (DHH) which is responsible for building and scaling AI capabilities across cross-functional teams and divisions. The team operates as an embedded strategic partner to the commercial organization providing thought leadership and delivering AI-powered solutions to marketing and cross-functional stakeholders. It shapes demand by translating business priorities into scalable AI products and agent-driven solutions that enhance decision-making and drive measurable business impact.

Role Overview

As the Associate Director Commercial AI Tech Lead you will define and lead the technical vision architecture and engineering execution for AI and Agentic AI products supporting our companys Commercial use cases in India. You will play a pivotal role in building and scaling enterprise-grade AI solutions including LLM-powered and agent-based systems from rapid prototyping through production will establish robust engineering foundations including reusable frameworks SDKs and best practices across LLMOps/MLOps while ensuring governance scalability and compliance. This role requires close collaboration with commercial stakeholders product and program leaders enterprise architecture and risk security and privacy teams to deliver impactful business-aligned solutions.

Key Responsibilities

• Own the reference architecture for Commercial AI Agentic AI solutions including patterns for RAG orchestration tool integration identity and observability across environments.
• Lead end-to-end delivery: discover design build deploy monitor iterate; ensure production readiness (performance reliability security cost).
• Engineer and ship agentic workflows (planning/reasoning tool/function calling reflection memory patterns guardrails) and harden them into enterprise-grade services.
• Establish LLMOps/MLOps operating practices: versioning/registry automated evaluation safe rollout/rollback monitoring and continuous improvement loops.
• Build and maintain reusable capabilities (templates SDKs integration patterns deployment scaffolds) that accelerate delivery across multiple commercial products/teams.
• Drive governance and risk controls in partnership with security/privacy/legal: responsible AI data privacy auditability human-in-the-loop controls and policy-based enforcement where needed.
• Implement evaluation & quality frameworks for LLM/agent systems (offline online) including guardrail testing prompt/retrieval regression tests and business KPI alignment.
• Own observability for AI services: tracing across prompt retrieval/tool calls response cost/usage dashboards incident triage postmortems and reliability improvements.
• Optimize latency quality and cost trade-offs through caching prompt/RAG tuning model routing and systematic performance benchmarking.
• Partner with business/product leaders to translate commercial problems into well-scoped AI roadmaps measurable outcomes and scalable platform components.
• Ensure solutions align with SDLC validation expectations appropriate for regulated environments; embed documentation and controls into delivery.
• Mentor and lead engineers/data scientists: design reviews coding standards coaching hiring input and building a high-performing delivery culture.

Technical Expertise

Agentic AI

• Hands-on experience designing multi-step agents with planning tool/function calling orchestration and reflection loops.
• Strong understanding of memory patterns (short-term long-term episodic) grounding strategies and failure-mode handling (loops hallucinations tool errors).
• Ability to implement guardrails: policy enforcement prompt injection defenses tool-use constraints HITL checkpoints and safe fallbacks.
• Expertise in agent evaluation: task success scoring trajectory analysis tool-call accuracy safety checks and regression automation.

LLM Engineering

• Proven experience with prompt engineering patterns structured outputs and reliability techniques (self-checks constrained decoding schemas).
• Deep practical knowledge of RAG: ingestion chunking embeddings vector search reranking context assembly and grounding metrics.
• Understanding of embeddings and vector databases and how to tune retrieval for precision/recall in enterprise settings.
• Awareness of fine-tuning approaches (when/why; trade-offs) and model selection strategies for regulated enterprise use.
• Strong focus on latency/cost optimization and observability for GenAI systems (tracing cost controls quality monitoring).
• Experience with Enterprise AI engine DataBricks AIBI DataIku AI and Others
• Architecture Integration & Platform Thinking
• Ability to define reference architectures for commercial AI solutions and scalable design patterns for multiple products/markets.
• Experience integrating AI/agent services with enterprise applications/APIs authentication/authorization logging and audit trails.
• Strong cloud-native engineering: containers orchestration CI/CD infrastructure automation and reliability practices.

Governance Risk & Responsible AI

• Working knowledge of model governance: documentation validation monitoring auditability and human oversight suitable for regulated environments.
• Data privacy/security-by-design: access controls encryption secure secrets and collaboration with InfoSec for audits and risk management.

Education Requirements

Masters (or equivalent) in Computer Science Artificial Intelligence Data Science Machine Learning Statistics Engineering or a related quantitative discipline with strong focus on AI/ML and modern data systems.

Required Experience and Skills

• 8 years in relevant experience across software engineering ML engineering MLOps/LLMOps or AI product engineering with hands on experience of production deployments at scale.
• 4 years leading teams and driving delivery across multiple stakeholders; proven ability to mentor and raise engineering standards.
• Strong hands-on proficiency in Python API/service development and modern engineering workflows (Git code reviews CI/CD).
• Proven experience implementing LLM applications and/or AI agents in production including evaluation and monitoring.
• Strong cloud-native foundation (Azure/AWS/GCP): containers orchestration automated testing release discipline and operational excellence.
• Demonstrated capability to establish reusable frameworks/SDKs integration patterns and scalable operating models for AI delivery.
• Excellent stakeholder communication: ability to explain trade-offs risks and outcomes clearly to technical and non-technical audiences

Required Skills:

Preferred Skills:

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