Technology Strategy Director

Freddie Mac•McLean, VA

1d•$209,000 - $313,000

About The Position

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. The role owns the technical core of SFA’s AI Systems Solutioning, including agent engineering and design. It covers AI system design and delivery from ideation and business casing through specifications, implementation, and deployment; evaluation harnesses and graded assessment sets for production readiness; and the operating layer that keeps agents accountable after launch. The position is approximately 70% hands-on architecture and coding and 30% leadership of GenAI engineering efforts and platform and vendor delivery partners. The Director works in a forward-deployed model with the divisional GenAI PMO, Enterprise Operations & Technology (EO&T), Digital Experience, and the Enterprise Business Rules platform team. The role establishes engineering practices and shared capabilities for moving agentic workflows from pilot to production, including orchestration, evaluation, retrieval, monitoring, and knowledge graphs. Agent-driven and rule-driven decisioning are expected to share a domain vocabulary, evaluation approach, and traceability model. This player/coach role leads engineering execution and delivery partners while remaining a senior hands-on engineer. It establishes a forward-deployed engagement model, spec-driven development, reusable engineering patterns, and a durable agentic workflow platform. The Director personally delivers initial production use cases in credit risk and quality control, and builds shared retrieval, orchestration, evaluation, observability, and governance capabilities for additional SFA use cases. The role embeds Responsible AI engineering, explainability, accountability, model risk management, data protection, and applicable regulatory requirements. Success in the first 12–18 months includes a production agentic workflow platform supporting at least two SFA use cases, standard evaluation and release gates, live observability and business-owner runbooks, a shared domain graph used by agentic workflows and the Business Rules platform, and reduced time-to-production for new agentic capabilities.

Requirements

12+ years building and operating production ML and AI systems in regulated environments.
5+ years leading engineering teams.
Demonstrated hands-on delivery of a multi-agent orchestration platform to production using LangGraph or an equivalent, including durable workflow state and declarative agent configuration.
Experience building evaluation harnesses, graded datasets, and release gates for LLM systems, and operating them with LangSmith or equivalent tracing and observability tooling.
Production-scale knowledge graph and hybrid RAG experience with Neo4j, vector search, and governed document corpora.
Track record of spec-driven development, writing specifications and acceptance criteria before building with AI coding assistants.
Production Python experience.
College degree or equivalent experience.
Forward-deployed approach: comfortable embedding with business teams and continuing to code.
Ability to demonstrate the value of delivered AI systems.
Plans before building and treats specifications and evaluation sets as core deliverables.
Systems-thinking approach that designs for future use cases as well as the current one.
Clear communication with engineers, business owners, and executives.

Nice To Haves

TypeScript/React experience.
AWS experience, including Bedrock, or Azure experience.
Advanced studies preferred.
Financial services or mortgage domain experience strongly preferred.

Responsibilities

Lead GenAI engineering efforts, execution, and delivery partners under a unified delivery model while remaining a senior hands-on engineer.
Establish a forward-deployed engagement model that embeds AI engineers with SFA business teams and is accountable for production delivery and post-launch behavior.
Institutionalize spec-driven development, ensuring specifications, acceptance criteria, and evaluation targets are written before implementation begins.
Establish and maintain a pattern library of reusable kickstarters, skills, and reference implementations, developed with AI coding assistants 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 in agent design, evaluation discipline, and production operations, and advise senior leadership on agentic readiness.
Architect and deliver a durable agentic workflow platform with persistent state, declarative agent configuration, tool and API integration, and agent interoperability.
Design a shared domain graph and knowledge and service layer to provide a common vocabulary across agents and rules.
Build assessment harnesses and graded datasets that gate promotion, capture traces, support regression checks, and guide prompt iteration and model selection.
Establish hybrid vector-and-graph retrieval over governed document content, with docs-as-code ingestion and approvals.
Engineer post-launch operations, including trace analysis and lineage, token and cost telemetry, SLO dashboards, drift detection, runbooks, monitoring, and documentation.
Personally deliver initial production use cases in credit risk and quality control from architecture through implementation, operations, and iteration.
Embed Responsible AI practices, including content guardrails, adversarial red-teaming, bias and fairness reviews, model validation, and governance artifacts for release gates.
Ensure every agent decision is traceable to source documents, prompt versions, and evaluation evidence.
Ensure compliance with model risk management, data protection, and regulatory requirements applicable to GSE decisioning.