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 ImpactFreddie 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 ImpactThis 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 checks; use the same harnesses to drive prompt iteration and model selection across providers
- Establish hybrid retrieval (vector plus graph) over a governed document substrate-Seller/Servicer Guide, policy, and procedural content-with docs-as-code ingestion and approvals, so every new use case inherits ingestion, guardrails, and observability
- Engineer the post-launch operating layer: trace analysis linking agent decisions back to source documents and prompt versions, token and cost telemetry per agent run, SLO dashboards (latency, cost, throughput), drift detection, and handoff packages (runbooks, monitoring, documentation) so business owners can run what was built
- Personally deliver the initial production use cases in credit risk and quality control end to end, from architecture through implementation, operations, and iteration
Technology & Engineering Practice- Agent systems: multi-agent orchestration on LangGraph and LangGraph Platform (AutoGen or equivalent), durable workflow state, declarative agent configuration, tool and API integration, agent interoperability (MCP, agent-to-agent handoffs), and vendor SDKs and agent development kits (AWS Bedrock, OpenAI, Anthropic)
- Retrieval and knowledge: hybrid RAG and Graph RAG, Neo4j knowledge graphs, vector search (OpenSearch or equivalent), shared S3 document substrates, and docs-as-code corpora with governed approvals
- Evaluation and operations: LangSmith for tracing, assessment datasets, and experiment tracking; graded assessment harnesses and hill-climbing improvement loops; release gates; trace lineage; token and cost telemetry; SLO dashboards; drift detection
- Engineering practice: spec-driven development, production Python, TypeScript/React, AI coding assistants, AWS and Azure, security architecture, and governance artifacts for regulated release gates
Governance, Risk, & Compliance- Embed Responsible AI engineering across all solutions: content guardrails, adversarial red-teaming, bias and fairness reviews, model validation, and the governance artifacts that clear release gates
- Ensure explainability and accountability: every agent decision traceable to its source documents, prompt version, and evaluation evidence
- Ensure compliance with model risk management, data protection, and regulatory requirements applicable to GSE decisioning
Qualifications- 12+ years building and operating production ML and AI systems in regulated environments, with 5+ years leading engineering teams
- Demonstrated delivery of a multi-agent orchestration platform to production as a hands-on engineer on LangGraph (or equivalent), including durable workflow state and declarative agent configuration
- Experience building evaluation harnesses, graded datasets, and release gates for LLM systems, and operating them in LangSmith (or equivalent tracing and observability tooling)
- Knowledge graph and hybrid RAG experience at production scale with Neo4j, vector search, and governed document corpora
- Track record of spec-driven development: writing the specification and acceptance criteria first, then building with AI coding assistants against them
- Production Python; TypeScript/React a plus; AWS (Bedrock) or Azure
- College degree or equivalent experience; advanced studies preferred
- Financial services or mortgage domain experience strongly preferred
Keys to Success in This Role- Forward-deployed instinct: comfortable embedding with business teams and staying in the code
- Insists on demonstrating value of AI Systems delivered
- Plans before building: treats the spec and the evaluation set as first-class deliverables, not afterthoughts
- Systems thinker who designs for the next use case, not just the current one
- Clear communicator with engineers, business owners, and executives
- First 12-18 months, success criteria includes:
- Agentic workflow platform in production with at least two SFA use cases (credit risk, quality control) running on shared orchestration
- Evaluation harness and release gates adopted as the standard for all SFA agent promotions
- Observability layer live-trace lineage, cost telemetry, SLO dashboards-with runbooks handed to business owners
- Shared domain graph in use by both agentic workflows and the Business Rules platform
- Measurable reduction in time-to-production for new agentic capabilities
Current Freddie Mac employees please apply through the internal career site.We consider all applicants for all positions without regard to gender, race, color, religion, national origin, age, marital status, veteran status, sexual orientation, gender identity/expression, physical and mental disability, pregnancy, ethnicity, genetic information or any other protected categories under applicable federal, state or local laws. We will ensure that individuals are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
A safe and secure environment is critical to Freddie Mac's business. This includes employee commitment to our acceptable use policy, applying a vigilance-first approach to work, supporting regulatory mandates, and using best practices to protect Freddie Mac from potential threats and risk. Employees exercise this responsibility by executing against policies and procedures and adhering to privacy & security obligations as required via training programs.
CA Applicants: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.
Notice to External Search Firms: Freddie Mac partners with BountyJobs for contingency search business through outside firms. Resumes received outside the BountyJobs system will be considered unsolicited and Freddie Mac will not be obligated to pay a placement fee. If interested in learning more, please visit and register with our referral code: MAC.
Time-type:Full time
FLSA Status:Exempt
Freddie Mac offers a comprehensive total rewards package to include competitive compensation and market-leading benefit programs. Information on these benefit programs is available on our Careers site.
This position has an annualized market-based salary range of $209,000 - $313,000 and is eligible to participate in the annual incentive program. The final salary offered will generally fall within this range and is dependent on various factors including but not limited to the responsibilities of the position, experience, skill set, internal pay equity and other relevant qualifications of the applicant.
