AI Solution Architect/Tech Lead

About this opportunity

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 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. 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