Technology Strategy Director

Opened Recently Location McLean, VA Category Technology Job Id JR17732

At Freddie Mac, our mission of Making Home Possible is what motivates us, and it’s at the core of everything we do. Since our charter in 1970, we have made home possible for more than 90 million families across the country. Join an organization where your work contributes to a greater purpose.

Position Overview:

Freddie Mac is seeking a hands-on AI engineering leader to serve as Director within the Single-Family Acquisitions (SFA) division, reporting to the Sr. Director of GenAI Engineering & Enterprise Rules Platform. This role owns the technical core of SFA’s AI Systems Solutioning, including Agent Engineering & Design. This includes (1) the overall design and execution of AI Systems development from Ideation & Business casing to Specification creation & execution through deployment, (2) the evaluation harnesses and graded assessment sets that determine whether an agent or AI System is ready for production, and (3) the operating layer that keeps agents accountable after go-live. The role is approximately 70% hands-on architecture and code and 30% leadership of a GenAI engineering efforts and platform and vendor delivery partners.

The Director is forward-deployed by design. The role partners with the divisional GenAI PMO, Enterprise Operations & Technology (EO&T), and Digital Experience, and works alongside the Enterprise Business Rules platform team so that agent-driven and rule-driven decisioning share a common domain vocabulary, evaluation approach, and traceability model.

Our Impact

Freddie Mac was established by Congress to provide liquidity, stability, and affordability to the U.S. housing market. Within Single-Family Acquisitions, our technology platforms and decisioning systems are foundational to ensuring loans sold to Freddie Mac meet eligibility, credit risk, and regulatory requirements at scale.

Your Impact

This role is a player / coach model. You will establish the engineering discipline that lets SFA move agentic workflows from pilot to production and keep them there: durable orchestration, accuracy that is measured rather than asserted, and operational transparency that satisfies both business owners and model risk oversight. Then use it. The capabilities built here—retrieval, orchestration, evaluation, and monitoring—become the shared substrate that every SFA use case inherits, so each delivery makes the next one faster. Building knowledge graphs that represent how the business functions and designing their use, matters.

Leadership

• Lead GenAI engineering efforts, execution, and delivery partners under a single delivery model, while remaining the senior hands-on engineer
• Stand up a forward-deployed engagement model (build, consult, advise, monitor) that embeds AI engineers alongside SFA business teams and is accountable both for what reaches production and for how it behaves afterward
• Institutionalize spec-driven development as the team’s way of working: specifications, acceptance criteria, and evaluation targets are written before build loops begin, so every capability is auditable from intent through implementation
• Establish and maintain the team’s pattern library—reusable kickstarters, skills, and reference implementations—built daily with AI coding assistants (GitHub Copilot, Amazon Kiro, OpenCode) under the spec-driven workflow
• Partner with the divisional GenAI PMO on capability roadmaps, prioritization, and enablement; with EO&T on reference architectures and platform standards; and with Digital on product ownership and delivery practices
• Mentor engineers on agent design, evaluation discipline, and production operations; serve as a credible technical voice with senior leadership on agentic readiness

Transformation & Delivery

• Architect and deliver a durable agentic workflow platform: a workflow engine with persistent state, declarative agent configuration so new agents onboard without engine changes, tool and API integration, and agent interoperability (MCP, agent-to-agent handoffs)
• Design a shared domain graph—a unified knowledge and service layer—as the common vocabulary across agents and rules, enabling validation across workstreams without coupling them to each other
• Build the assessment harnesses and graded datasets that gate promotion: scored runs, full trace capture, and regression