_{29th April, 2026}

Role: Program Manager - AI Engineering
Location: Summerville, SC (Hybrid - 2 days onsite).
 
Job Description:
The Program Manager - AI Engineering will serve as the organization's senior technical leader for AI, responsible for the AI capability stack, setting the standards that govern how AI solutions are built, and translating strategic priorities into a credible, phased delivery plan.
 
This is a hands-on role, as the AI Engineering lead, they will prototype and build solutions, evaluate tooling, and directly shape how AI is built and delivered. This person must be as comfortable presenting architecture decisions to non-technical leadership as they are working alongside engineers.
 
This role works in close partnership with the AI Transformation Lead to facilitate a clear handoff and an agreed collaboration cadence between the technical and adoption tracks.
 
Key Responsibilities:
 
AI Architecture

• Provide guidance on architectural direction for the AI capability stack on Microsoft Azure, including data flow patterns, integration approach, and selection of appropriate AI services in partnership with technical leadership.
• Work with Technical Leadership to make and document build vs. buy decisions, including evaluation of existing vendor relationships and third-party AI capabilities.
• Ensure compliance requirements, including FedRAMP, are identified early and designed in, engaging internal and vendor resources where deeper expertise is needed.
• Provide guidance on AI standards covering solution design, testing, documentation, and handoff practices
• Evaluate the AskSage and Copilot deployments and advise on the role within the broader AI stack.
• Advise on data architecture patterns appropriate for AI workloads, including retrieval-augmented generation (RAG), document ingestion pipelines, and knowledge retrieval.

Delivery & Mentorship

• Build working prototypes of prioritized AI capabilities to validate feasibility and demonstrate technical direction.
• Evaluate the current AI capabilities and recommend a plan to grow capabilities through training or augmentation
• Work with internal and external team members to determine the best delivery strategy.
• Establish a working relationship with the AI Transformation Lead, including clear handoffs and an agreed collaboration rhythm.

Core Technical Requirements:
 
LLM Application Development

• Hands-on experience building production LLM-based applications, including prompt engineering, retrieval-augmented generation (RAG), and document processing pipelines.
• Experience with knowledge retrieval systems using vector search and semantic indexing.
• Experience with intelligent document processing, including OCR, document classification, and structured data extraction from unstructured sources.
• Practical understanding of when LLM-based approaches are the right solution and when simpler methods are more appropriate, and the ability to make that case clearly.

Microsoft Azure

• Hands-on experience with Azure AI Services: Azure OpenAI Service, Azure AI Search, Azure Document Intelligence, and Azure Cognitive Services.
• Working knowledge of Azure data and compute infrastructure relevant to AI workloads: Azure Blob Storage, Azure Data Lake, Azure Functions, and Azure Container Apps.
• Experience with Azure DevOps for solution delivery and Azure AI Studio for model management and deployment.

Compliance & Regulated Environments

• Demonstrated experience designing AI solutions for FedRAMP-compliant or similarly regulated environments.
• Ability to translate compliance requirements into concrete architecture decisions and communicate them clearly to non-technical stakeholders.

Software Engineering Fundamentals

• Proficiency in Python for AI application development.
• Experience designing APIs that expose AI capabilities to downstream applications.
• Familiarity with C# or TypeScript in Azure/.NET environments is a plus.

Additional Technical Skills:
The following skills are valued and will strengthen a candidate's profile.

• Agentic workflow design, including multi-step orchestration using frameworks such as LangChain, Semantic Kernel, or AutoGen.
• LLMOps practices, including prompt versioning, output observability, and evaluation frameworks.
• Containerization (Docker, Kubernetes) and infrastructure-as-code (Terraform, Bicep).
• Microsoft Power Platform (Power Automate, Copilot Studio) as a delivery layer for workflow automation.
• Model Context Protocol (MCP) or equivalent standards for connecting AI models to organizational data and tools.

Qualifications:
Required

• 7+ years of experience in software engineering, solutions architecture, or a related technical field, with at least 3 years focused on AI or machine learning systems.
• Demonstrated track record of designing and delivering AI solutions in production — not just prototypes or proofs of concept.
• Experience setting technical direction for small engineering teams, including standards, patterns, and review practices.
• Hands-on experience with Microsoft Azure, particularly Azure AI Services and Azure OpenAI.
• Strong communication skills, able to translate complex technical decisions for non-technical audiences, and build confidence with leadership teams that are new to AI.
• Bachelor's degree in computer science, data science, software engineering, or a related field; advanced degree preferred but not required, where equivalent experience is demonstrated.

Preferred:

• Experience in a government contracting, regulated services, or similarly compliance-driven environment.
• Familiarity with FedRAMP requirements and their architectural implications.
• Prior experience evaluating and onboarding AI vendors or managing outsourced delivery relationships.
• Microsoft Azure certifications (e.g., Azure Solutions Architect Expert, Azure AI Engineer Associate).

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