AI Architect

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.