Lead AI Architect/Strategist
$150,000–$200,000 year
RemoteUnited States
Job Summary
Define and execute AI architecture strategies, technical roadmaps, and modernization approaches aligned with organizational goals. Design scalable AI platforms leveraging Generative AI, LLMs, RAG, AI agents, and machine learning to integrate models, data platforms, and enterprise applications. Lead technical evaluations of emerging AI technologies and guide teams through implementation from proof-of-concept to production deployment. Establish AI governance practices supporting security, compliance, and responsible AI while collaborating with engineering, data, and product teams. Communicate complex AI concepts to technical and executive stakeholders. Provide technical leadership, mentorship, and guidance on architecture patterns and engineering best practices.
Required Qualifications
- Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or related technical field
- 10+ years of experience in software engineering, enterprise architecture, solution architecture, data architecture, or technology transformation
- 5+ years of experience designing and implementing AI/ML solutions, AI platforms, or intelligent automation solutions
- Strong experience architecting enterprise-scale technology solutions and distributed systems
- Proven experience with Generative AI, Large Language Models (LLMs), and AI application architectures
- Hands-on experience with AI concepts and technologies including Retrieval-Augmented Generation (RAG), AI Agents / Agentic AI, prompt engineering, vector databases and embeddings, model orchestration, AI governance and lifecycle management
- Experience with cloud AI platforms such as: Azure OpenAI / Azure AI Services, AWS Bedrock, Google Vertex AI
- Experience with modern AI frameworks and tools such as: Lang Chain, Lang Graph' Llama Index, Semantic Kernel, OpenAI frameworks
- Strong programming experience with Python or other modern programming languages
- Experience with data architecture, APIs, data pipelines, and enterprise system integration
- Understanding of MLOps/LLMOps practices, including monitoring, evaluation, deployment, and governance
- Ability to communicate complex technical concepts effectively with both technical and non-technical audiences
Desired Qualifications
- Experience supporting Federal Government, healthcare, or other highly regulated environments
- Experience designing secure AI solutions within enterprise environments
- Experience with Kubernetes, containerized applications, cloud-native architectures, and modern DevOps practices
- Experience with enterprise architecture frameworks such as TOGAF
- Experience leading AI transformation initiatives from strategy through implementation
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