Roles & Responsibilities

We are seeking an experienced AI Technical Lead / AI Solution Architect to design and deliver production-grade AI/ML solutions within a government or highly regulated environment. The role combines hands-on AI engineering, solution architecture, Generative AI expertise and technical leadership. The successful candidate will define technical solutions, guide implementation, review architecture and code, and technically lead AI delivery teams.

Key Requirements

• Proven hands-on experience designing and delivering AI/ML solutions into production.
• Strong practical knowledge of Machine Learning, Generative AI/LLMs and Agentic AI.
• Strong Python development skills, with the ability to prototype, review and guide production AI/ML implementations.
• Practical experience with RAG, embeddings, vector search, prompt engineering, tool/function calling, agent orchestration and LLM evaluation.
• Strong AI solution architecture and integration capabilities, including scalability, security, reliability, performance and cost considerations.
• Experience designing AI solutions across public cloud, hybrid, private or sovereign cloud environments.
• Strong Microsoft Azure experience, ideally including Azure OpenAI, Azure AI Foundry, Azure AI Search and Azure Machine Learning.
• Understanding of MLOps/LLMOps, including deployment, evaluation, monitoring, versioning and production lifecycle management.
• Strong understanding of AI security, governance, responsible AI, data privacy and data sovereignty.
• Experience with modern software engineering practices including APIs, microservices, containers, Kubernetes and CI/CD.
• Proven experience technically leading and mentoring AI Developers, ML Engineers and Data Scientists.

Technical Stack

Experience with relevant technologies across:

• GenAI/LLM: Azure OpenAI, Azure AI Foundry, OpenAI-compatible APIs, Hugging Face or equivalent.
• RAG & Search: Azure AI Search, vector databases, embeddings and semantic search.
• Agentic AI: LangChain, LangGraph, Semantic Kernel, LlamaIndex or equivalent agent frameworks.
• ML/MLOps: Azure Machine Learning, MLflow or equivalent.
• Cloud & Engineering: Microsoft Azure, REST APIs, microservices, Docker, Kubernetes, Git and CI/CD.
• Data: SQL/NoSQL, data pipelines, data lakes/lakehouses; Databricks, Spark or Microsoft Fabric are advantageous.

Depth of technical capability is more important than experience with every named technology.

Preferred Experience

• Government or highly regulated environments.
• Experience with data residency, sovereignty, security and regulatory requirements.
• AI governance, cybersecurity and responsible AI.
• Production MLOps / LLMOps.
• Computer Vision.
• High-volume, scalable or real-time AI solutions.
• Open-source or self-hosted LLMs.
• Hybrid, private or sovereign cloud architectures