KEY RESPONSIBILITIES :

Embedded Client Delivery :

- Embed directly within pharma client organisations - operating as a trusted technical peer, not a vendor - and own the design, build, and deployment of production AI systems end-to-end on the client's own infrastructure.

- Lead the technical workstream and direct teams of engineers - Agent Pipeline Engineers and AI-Augmented Engineers - under your architecture and delivery ownership.

- Hold the technical client relationship at Director, VP, and CDO level : scoping problems, presenting architecture trade-offs, defending design decisions under scrutiny, and translating technical outcomes into business language.

AI Architecture and Engineering :

- Architect multi-agent AI systems for pharma environments - spanning orchestration patterns, tool and function integration, retrieval-augmented generation, memory architectures, human-in-the-loop design, evaluation pipelines, and production MLOps.

Tech Stack :

- Databricks (Delta Lake, Mosaic AI, Genie), Snowflake (Snowpark, Cortex AI, Cortex Analyst), AWS (Bedrock Agents, SageMaker), and the Claude and Anthropic API stack with Model Context Protocol.

- Design and implement AI evaluation frameworks appropriate for regulated pharma environments - probabilistic quality thresholds, RAGAS, LLM-as-judge, adversarial red-teaming, and audit-trail-compliant output governance.

- Ensure systems are production-grade : observable, maintainable, secure, and compliant with pharma data governance requirements including HIPAA, GDPR, and applicable FDA AI/ML guidance.

- Stay current with the agentic AI ecosystem - frameworks, model capabilities, evaluation techniques, and orchestration protocols - and translate emerging capability into deployment-relevant technical decisions.

Pharma Domain Translation :

- Translate pharma commercial and clinical business problems into AI-solvable architectures without requiring a domain primer from the client - the depth of your domain expertise is part of what you bring to the engagement.

- Validate that AI outputs are accurate against pharma business logic and commercial or clinical norms - not just technically correct but domain-defensible and explainable to the end users who act on them.

- Serve as the connective layer between the client's business problem and the technical solution, eliminating the scoping ambiguity that causes most pharma AI deployments to stall before production.