About the role
Responsibilities:
- Design end-to-end solutions for client engagements, from data and models to deployment, evaluation and operations.
- Lead RFP responses and proposals: solution outline, team shape, estimates, risks and success measures.
- Act as the senior technical voice with client sponsors, IT, security and business stakeholders.
- Set engineering standards for your pod and review critical designs and code.
- Hire, coach and grow your Lead, Senior Engineers and AI Engineers.
- Build reusable assets (accelerators, reference architectures, evaluation harnesses) that make the next engagement faster.
- • Re-plan calmly when client scope, platform or constraints change mid-engagement. Pod focus: Data and Decisions
- • Lakehouse and warehouse platforms (Databricks, Snowflake or similar), including migrations between them.
- • Data modelling, semantic layers and governance that reconcile to business definitions.
- • Forecasting, supply chain planning and decision-support agents built on top of the data platform.
Pod focus: Agentic Systems
- Agent platforms and production agents across AWS, Azure and GCP, with cloud-agnostic core design.
- Tool design, permissions, human approval gates, tracing and evaluation for agents.
- IT and operations agents, LLMOps, observability and integration with enterprise systems.
Requirements:
- Primary Skillsets: Python, Generative AI, Agentic AI, LLM, experience designing and developing production-grade AI solutions, AWS/Azure/GCP, LLMOps, Data Engineering (Databricks/Snowflake), Regulated GenAI, Data and Decisions, and Agentic Systems.
- Preferred: Pharma or life sciences experience, especially in regulated (GxP) environments, and experience setting up a team or practice from scratch.