Principal AI Platform / Agentic AI Architect

Project Overview:

We are seeking a highly experienced and hands-on Principal AI/ML Architect & Applied AI Lead to drive the design, development, and operationalization of enterprise-scale AI systems across research and production environments.

This role combines deep technical expertise in Machine Learning, Generative AI, distributed data systems, and cloud-native architectures with strategic leadership capabilities. The ideal candidate will lead complex AI initiatives end-to-end — from experimentation and research to scalable deployment in global enterprise environments.

The position requires a strong balance between:

• technical leadership,
• hands-on implementation,
• AI strategy,
• cross-functional collaboration,
• and mentoring of engineering and data science teams.

Responsibilities:

• Lead the design and implementation of AI/ML solutions across multiple business domains.
• Drive enterprise adoption of Large Language Models (LLMs), Generative AI, NLP/NLU, and advanced analytics solutions.
• Define AI architecture standards, MLOps best practices, and scalable deployment strategies.
• Evaluate emerging AI technologies and identify opportunities for innovation and operational impact.
• Architect scalable distributed data-processing systems capable of handling large-scale datasets and real-time pipelines.
• Model-portable design: gateway-based LLM access (e.g. Bedrock, LLM-gateway/router products), designing so models can be swapped without re-architecture. This is a live workstream on the account:

-Platform capability migration: experience consolidating/migrating AI capabilities from one platform onto another (strangler-style,incremental), not greenfield-only.

- Architecture governance: design decision records, keeping design docs and code in sync, documenting so knowledge survives team rotation.

• Integrate an AI platform with an enterprise lakehouse (Databricks/Unity Catalog): design the interface to the semantic layer, reconcile access-control models (UC row-level security vs application-layer scoping), plan migration sequencin
• Lead cloud migration and modernisation initiatives from platform to platform
• Ensure reliability, scalability, observability, and cost-efficiency of AI infrastructure.
• Design and implement enterprise-grade chatbot and conversational AI platforms.
• Lead development of Retrieval-Augmented Generation (RAG), agentic workflows, and LLM orchestration systems.
• Define governance, evaluation, and monitoring strategies for GenAI systems.
• Lead cross-functional teams composed of data scientists, ML engineers, software engineers, and business stakeholders.
• Mentor engineers in AI/ML best practices, architecture, and software engineering standards.
• Coordinate global AI initiatives across distributed teams and multiple geographies.
• Communicate technical concepts effectively to executive and non-technical audiences.
• Support innovation programs and AI adoption strategies across the organization.

Requirements:

• Hands-on architecture of production LLM/agentic systems: agent runtimes, agentic RAG, tool/function-calling integration, multi-agent orchestration. Cloud agent platform experience (AWS Bedrock/AgentCore or equivalent).
• LLM evaluation and observability in production: eval pipelines, regression gates, tracing, dashboards (not just offline notebooks).
• Security and access-control literacy for AI platforms: enterprise identity (Entra/OAuth), role/group-based data access, permission-aware retrieval, auditability.
• Working fluency with AI coding agents (Claude Code / Copilot-class) in own workflow, and ability to mentor a team into agent-native ways of working.
• Consulting posture: embedded advisory work with client stakeholders, able to align and decide with executives, not only build.
• English B2/C1 (daily EU + US/Canada stakeholder communication), proven multinational experience.
• 5+ years of experience in AI/ML, data science, or distributed systems engineering.
• Proven experience designing and deploying production-grade AI solutions at enterprise scale.
• Experience leading global or distributed technical teams.
• Demonstrated success delivering AI transformation initiatives
• SQL / NoSQL databases
• Docker
• Kubernetes
• CI/CD pipelines
• Infrastructure-as-Code
• MLOps frameworks
• Python