Principal AI Architect and Enterprise AI Transformation Lead

Unissant•Ashburn, VA

5d•Onsite

About The Position

Unissant is seeking a Principal AI Architect and Enterprise AI Transformation Lead to join their team and support a federal client in Ashburn, VA. The ideal candidate is a hands-on AI leader with deep technical expertise, enterprise architecture, AI strategy, and customer engagement skills. This individual will lead the adoption and operationalization of AI capabilities, shape solution architecture, develop prototypes, and guide secure deployment. The candidate will bring experience from a commercial AI company or comparable fast-moving commercial AI environment and apply those practices to secure federal missions. They will build and demonstrate AI solutions using Generative AI, Large Language Models, Agentic AI, Retrieval-Augmented Generation, machine learning, and cloud-native technologies. The role requires comfort in coding, designing enterprise architectures, leading customer demonstrations and workshops, guiding technical teams, and advising executives on AI strategy, adoption, and mission value.

Requirements

Minimum 15 years of progressive experience in software engineering, enterprise architecture, cloud architecture, systems engineering, data engineering, or related technical disciplines, including leadership of complex enterprise technology initiatives.
Minimum 8 years of experience designing and delivering AI, machine learning, data science, or advanced analytics solutions, including recent hands-on experience building and deploying enterprise AI applications.
Demonstrated success serving as a Principal AI Architect, Enterprise AI Architect, AI Solutions Architect, Chief Architect, Lead AI Engineer, or equivalent senior technical leader responsible for enterprise-scale AI solutions.
Demonstrated experience personally designing, coding, prototyping, and deploying functional AI applications while also providing architecture and strategic leadership.
Demonstrated hands-on experience at a commercial AI company or comparable commercial technology organization developing, implementing, or scaling AI products and solutions; experience applying commercial AI practices within federal, national security, regulated, or mission-critical environments is strongly preferred.
Advanced hands-on programming experience with Python and relevant AI, machine learning, data science, and API development libraries.
Experience developing Generative AI applications using Large Language Models, prompt engineering, Retrieval-Augmented Generation, tool calling, AI agents, and orchestration frameworks.
Hands-on experience with leading enterprise AI and machine learning platforms and frameworks, such as Azure AI, AWS Bedrock, Amazon SageMaker, Databricks, MLflow, Hugging Face, LangChain, LangGraph, LlamaIndex, or comparable technologies.
Experience designing and implementing vector databases, embedding pipelines, semantic search, knowledge retrieval, document processing, and enterprise RAG solutions.
Experience designing autonomous or human-supervised AI agents and multi-agent workflows.
Experience implementing AI evaluation methods for accuracy, groundedness, relevance, safety, latency, reliability, and overall solution performance.
Experience implementing MLOps, GenAIOps, model monitoring, AI governance, and automated deployment pipelines.
Experience with Docker, Kubernetes, APIs, microservices, CI/CD pipelines, infrastructure automation, and cloud-native application development.
Strong understanding of machine learning, deep learning, natural language processing, Generative AI, Agentic AI, data science, and enterprise architecture.
Experience architecting complex hybrid-cloud, multi-cloud, and on-premises environments supporting regulated or mission-critical operations.
Experience developing enterprise roadmaps, reference architectures, technology standards, reusable solution patterns, and technical governance processes.
Strong knowledge of enterprise architecture frameworks such as TOGAF, FEAF, DoDAF, Zachman, or equivalent methodologies.
Strong understanding of responsible AI, cybersecurity, data privacy, access controls, model risk, compliance, and secure AI development practices.
Demonstrated ability to move AI solutions from experimentation and proof of concept through deployment and operational adoption.
Strong executive presence and the ability to translate complex AI concepts into clear recommendations, tradeoffs, risks, adoption plans, and business or mission value for senior stakeholders.
Excellent written and verbal communication skills with the ability to engage technical and executive audiences.
Proven ability to influence without direct authority and work effectively across commercial AI organizations, federal customers, internal teams, partners, and external organizations.
Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, Mathematics, Information Systems, or a related field is required.
DHS CBP or TS/SCI clearance is required.

Nice To Haves

A master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline is preferred.
Active security clearance is preferred.

Responsibilities

Lead enterprise AI strategy, architecture, adoption, and roadmaps while translating priorities into executable initiatives across programs, customers, and technical teams.
Identify high-value opportunities where AI can improve mission performance, decision-making, operational efficiency, automation, and customer services.
Evaluate foundation models, AI platforms, development frameworks, and emerging technologies through hands-on experimentation and benchmarking, then recommend practical options based on mission fit, security, scalability, cost, and implementation risk.
Rapidly design, build, test, and demonstrate AI and machine learning prototypes that validate high-value mission and business use cases and establish a path to production.
Convert validated prototypes into secure, scalable, production-ready solutions using Generative AI, Large Language Models, Agentic AI, Retrieval-Augmented Generation, intelligent automation, and related enterprise AI technologies.
Develop AI applications and services using Python, APIs, cloud-native services, and modern AI frameworks.
Design and implement enterprise RAG solutions, including document ingestion, embeddings, vector databases, semantic search, grounding, retrieval, orchestration, and response evaluation.
Design and develop AI agents and multi-agent workflows that integrate with enterprise applications, data sources, APIs, and business processes.
Architect secure and scalable AI platforms across hybrid-cloud, multi-cloud, and on-premises environments.
Design and implement MLOps and GenAIOps pipelines supporting model development, deployment, evaluation, monitoring, governance, and lifecycle management.
Build and integrate data pipelines, vector databases, model endpoints, agent frameworks, APIs, and cloud-native AI services.
Establish technical approaches for prompt engineering, model selection, fine-tuning, grounding, human-in-the-loop workflows, guardrails, and AI evaluation.
Partner with engineering, cybersecurity, data, cloud, DevSecOps, operations, and enterprise architecture teams to integrate AI capabilities with existing systems and target-state architectures.
Conduct architecture reviews, code reviews, technical assessments, and solution evaluations to ensure alignment with security, privacy, governance, interoperability, performance, and scalability requirements.
Define reusable AI reference architectures, development patterns, technical standards, and implementation best practices.
Provide hands-on technical leadership by contributing to solution design and implementation, resolving complex technical issues, reviewing engineering work, and mentoring delivery teams.
Lead customer demonstrations, technical workshops, discovery sessions, solution reviews, and executive briefings that translate AI capabilities into clear mission use cases, adoption paths, and measurable business value.
Provide technical leadership for business development, capture, proposals, solutioning, and customer demonstrations by shaping credible AI approaches, architectures, prototypes, and implementation plans.
Apply commercial AI product-development, rapid experimentation, and deployment practices from a commercial AI company or comparable fast-moving technology organization to accelerate secure AI adoption in federal and national security environments.
Remain current on developments in Generative AI, Agentic AI, machine learning, cloud AI platforms, data engineering, and responsible AI.