Associate Director, Commercial AI – Tech Lead

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.

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 company’s 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 deployment.You 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.
 

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  Own the reference architecture for Commercial AI + Agentic AI solutions, including patterns for RAG, orchestration, tool integration, identity, and observability across environments.

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  Lead end-to-end delivery: discover → design → build → deploy → monitor → iterate; ensure production readiness (performance, reliability, security, cost).

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  Engineer and ship agentic workflows (planning/reasoning, tool/function calling, reflection, memory patterns, guardrails) and harden them into enterprise-grade services.

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  Establish LLMOps/MLOps operating practices: versioning/registry, automated evaluation, safe rollout/rollback, monitoring, and continuous improvement loops.

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  Build and maintain reusable capabilities (templates, SDKs, integration patterns, deployment scaffolds) that accelerate delivery across multiple commercial products/teams.

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  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.

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  Implement evaluation & quality frameworks for LLM/agent systems (offline + online), including guardrail testing, prompt/retrieval regression tests, and business KPI alignment.

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  Own observability for AI services: tracing across prompt → retrieval/tool calls → response, cost/usage dashboards, incident triage, postmortems, and reliability improvements.

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  Optimize latency, quality, and cost trade-offs through caching, prompt/RAG tuning, model routing, and systematic performance benchmarking.

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  Partner with business/product leaders to translate commercial problems into well-scoped AI roadmaps, measurable outcomes, and scalable platform components.

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  Ensure solutions align with SDLC + validation expectations appropriate for regulated environments; embed documentation and controls into delivery.

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  Mentor and lead engineers/data scientists: design reviews, coding standards, coaching, hiring input, and building a high-performing delivery culture.

• 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.

• 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.

• 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.

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.

• 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

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