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 modeling, semantic layers, and governance that reconcile to business definitions.
• Forecasting, supply chain planning, and decision-support agents built on top of the data platform.

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 Skill Sets: 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.