Senior Director, Software Engineering

Freddie Mac•McLean, VA

2d•$228,000 - $342,000

About The Position

Freddie Mac is seeking a Sr. Director, Generative AI Development to lead a team of engineers building and operating internal Generative AI capabilities, including the company’s internal chatbot and additional Gen AI-enabled products. This leader will drive delivery excellence, engineering rigor, and secure-by-design implementation across the full software lifecycle—partnering closely with Product, Architecture, Cybersecurity, Data, Model Risk, Legal/Compliance, and business stakeholders.

Requirements

12+ years of software engineering experience, including 5+ years leading engineering teams/managers.
Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
Proven experience delivering production-grade AI/ML or GenAI-enabled applications at enterprise scale.
Strong knowledge of modern cloud-native engineering (APIs, microservices, containers, CI/CD, IaC).
Recent hands-on coding experience.
Experience with secure software development practices and enterprise risk/compliance expectations.
Strong stakeholder management skills; ability to influence across product, security, and governance functions.

Nice To Haves

Experience implementing RAG with vector databases and enterprise search.
Familiarity with LLM evaluation methods and automated testing approaches for GenAI.
Experience with MLOps/LLMOps, model monitoring, and cost optimization.
Background in regulated industries (financial services, mortgage, insurance) and related control environments.

Responsibilities

Lead, coach, and grow a high-performing GenAI engineering team; set clear expectations, career paths, and performance standards.
Own delivery for internal AI chatbot and GenAI services: roadmap execution, sprint planning, dependency management, and release readiness.
Establish engineering standards for GenAI applications: prompt/service design patterns, evaluation frameworks, observability, and incident response.
Drive architecture and implementation for LLM-enabled systems (e.g., RAG, tool/function calling, agentic workflows) aligned to enterprise standards.
Ensure security, privacy, and compliance requirements are built-in (e.g., data handling, access controls, auditability, retention).
Partner with Model Risk Management and governance stakeholders to support appropriate documentation, testing, and controls for GenAI solutions.
Implement and monitor quality metrics (latency, cost per interaction, groundedness, hallucination rate, user satisfaction) and continuously improve.
Manage vendor/platform dependencies (LLM providers, orchestration frameworks, vector stores) and guide build-vs-buy decisions.
Communicate status, risks, and tradeoffs to senior technology and business leaders; translate business needs into engineering plans.