Freddie Mac
Overview
Join to apply for the Gen AI Engineering Manager role at Freddie Mac. Freddie Mac is seeking a visionary Gen AI Engineering Manager to architect and deliver next-generation GenAI applications, agentic workflows, and AI-powered platforms. You will lead a cross-functional team of data scientists and full stack engineers to address diverse and complex business use cases, combining hands-on technical leadership in scalable AI agent design, model development, production-grade deployment, and full-stack solution delivery. Our Agentic AI initiatives aim to transform business operations and prepare Freddie Mac for the future of intelligent, autonomous technology. Responsibilities
Idea Incubation & Experimentation: Collaborate with business stakeholders to identify innovative ideas using data science and GenAI experimentation. Rapidly prototype solutions to validate hypotheses and quantify business impact. MVP Development: Lead the development of Minimum Viable Products (MVPs) based on validated experiments, ensuring value delivery and scalability. Productionalization: Transition MVPs to production-ready GenAI solutions with robust quality, security, and operational practices. GenAI Agentic Design: Architect scalable AI agents and agentic workflows tailored to complex business challenges. Model Development & Optimization: Develop and fine-tune lightweight LLMs, evaluate models (e.g., Claude, Azure OpenAI) and open-source alternatives. RAG & GraphRAG Systems: Design and deploy RAG/GraphRAG solutions using vector databases and enterprise knowledge bases. Enterprise Data Curation: Curate enterprise data to support robust knowledge retrieval. Agent Communication: Implement Model Context Protocol (MCP) and Agent-to-Agent communication patterns. Notebook Infrastructure: Build and maintain Jupyter-based notebooks on platforms like SageMaker and Kubeflow on Kubernetes (EKS). Full-Stack Collaboration: Collaborate with UI engineers, microservice developers, designers, and data engineers to deliver seamless GenAI experiences. API Integration: Integrate GenAI solutions with enterprise platforms using API-based methods. Production Validation: Establish validation procedures with evaluation frameworks, bias mitigation, safety protocols, and guardrails. Data Ingestion Pipelines: Build robust ingestion pipelines to extract, chunk, enrich, and anonymize data from PDFs, video, and audio for GenAI workflows. Multimodal Pipelines: Orchestrate multi-modal pipelines for automated ETL/ELT on unstructured media. Embeddings & Vector Stores: Implement embeddings and vector stores to support advanced RAG architectures. Requirements & Qualifications
Bachelor's in Computer Science, AI, Data Science, or related field. Master's preferred. 8+ years in Software Engineering, with 5+ years in data science and 1–2 years in applied GenAI or LLM-based solutions. 2+ years of leadership experience. Experience leading cross-functional agile teams of data scientists and full-stack engineers. Deep expertise in prompt engineering, fine-tuning, RAG/GraphRAG, vector databases, and multi-modal models. Cloud-native AI development experience (e.g., AWS SageMaker, Bedrock, MLFlow, Kubeflow on EKS). Strong Python skills and ML libraries (Transformers, LangChain, etc.). Understanding of GenAI system patterns, architectural best practices, and evaluation frameworks for bias mitigation and safety. Experience with embedding models, vector stores, multimodal data pipelines, and production-grade validation. Excellent communication skills; ability to translate technical concepts for non-technical stakeholders. Preferred Qualifications
Experience in regulated financial environments with compliance automation. Prior work implementing agentic workflows and AI-powered enterprise platforms. Keys to Success
Deliver Predictably: Ship high-quality, secure, and compliant Agentic AI solutions on time. Innovate Rapidly: Incubate ideas, develop MVPs, and scale to production. Lead Technically: Architect scalable agentic AI systems and stay ahead of tech trends. Drive Business Impact: Align AI initiatives with business goals and communicate ROI. Engage Stakeholders: Build partnerships and communicate proactively across teams. Empower Your Team: Foster psychological safety, ownership, and growth. Balance Innovation & Compliance: Deliver advanced solutions while meeting regulatory standards. Notice
Current Freddie Mac employees please apply through the internal career site. We are an equal opportunity employer. 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, disability, pregnancy, or any other protected status. Reasonable accommodations are available on request. Salary & Location
This position has an annualized market-based salary range and is eligible for incentive programs. The final salary offered will depend on responsibilities, experience, and qualifications. Washington, DC area and remote options may apply per policy.
#J-18808-Ljbffr
Join to apply for the Gen AI Engineering Manager role at Freddie Mac. Freddie Mac is seeking a visionary Gen AI Engineering Manager to architect and deliver next-generation GenAI applications, agentic workflows, and AI-powered platforms. You will lead a cross-functional team of data scientists and full stack engineers to address diverse and complex business use cases, combining hands-on technical leadership in scalable AI agent design, model development, production-grade deployment, and full-stack solution delivery. Our Agentic AI initiatives aim to transform business operations and prepare Freddie Mac for the future of intelligent, autonomous technology. Responsibilities
Idea Incubation & Experimentation: Collaborate with business stakeholders to identify innovative ideas using data science and GenAI experimentation. Rapidly prototype solutions to validate hypotheses and quantify business impact. MVP Development: Lead the development of Minimum Viable Products (MVPs) based on validated experiments, ensuring value delivery and scalability. Productionalization: Transition MVPs to production-ready GenAI solutions with robust quality, security, and operational practices. GenAI Agentic Design: Architect scalable AI agents and agentic workflows tailored to complex business challenges. Model Development & Optimization: Develop and fine-tune lightweight LLMs, evaluate models (e.g., Claude, Azure OpenAI) and open-source alternatives. RAG & GraphRAG Systems: Design and deploy RAG/GraphRAG solutions using vector databases and enterprise knowledge bases. Enterprise Data Curation: Curate enterprise data to support robust knowledge retrieval. Agent Communication: Implement Model Context Protocol (MCP) and Agent-to-Agent communication patterns. Notebook Infrastructure: Build and maintain Jupyter-based notebooks on platforms like SageMaker and Kubeflow on Kubernetes (EKS). Full-Stack Collaboration: Collaborate with UI engineers, microservice developers, designers, and data engineers to deliver seamless GenAI experiences. API Integration: Integrate GenAI solutions with enterprise platforms using API-based methods. Production Validation: Establish validation procedures with evaluation frameworks, bias mitigation, safety protocols, and guardrails. Data Ingestion Pipelines: Build robust ingestion pipelines to extract, chunk, enrich, and anonymize data from PDFs, video, and audio for GenAI workflows. Multimodal Pipelines: Orchestrate multi-modal pipelines for automated ETL/ELT on unstructured media. Embeddings & Vector Stores: Implement embeddings and vector stores to support advanced RAG architectures. Requirements & Qualifications
Bachelor's in Computer Science, AI, Data Science, or related field. Master's preferred. 8+ years in Software Engineering, with 5+ years in data science and 1–2 years in applied GenAI or LLM-based solutions. 2+ years of leadership experience. Experience leading cross-functional agile teams of data scientists and full-stack engineers. Deep expertise in prompt engineering, fine-tuning, RAG/GraphRAG, vector databases, and multi-modal models. Cloud-native AI development experience (e.g., AWS SageMaker, Bedrock, MLFlow, Kubeflow on EKS). Strong Python skills and ML libraries (Transformers, LangChain, etc.). Understanding of GenAI system patterns, architectural best practices, and evaluation frameworks for bias mitigation and safety. Experience with embedding models, vector stores, multimodal data pipelines, and production-grade validation. Excellent communication skills; ability to translate technical concepts for non-technical stakeholders. Preferred Qualifications
Experience in regulated financial environments with compliance automation. Prior work implementing agentic workflows and AI-powered enterprise platforms. Keys to Success
Deliver Predictably: Ship high-quality, secure, and compliant Agentic AI solutions on time. Innovate Rapidly: Incubate ideas, develop MVPs, and scale to production. Lead Technically: Architect scalable agentic AI systems and stay ahead of tech trends. Drive Business Impact: Align AI initiatives with business goals and communicate ROI. Engage Stakeholders: Build partnerships and communicate proactively across teams. Empower Your Team: Foster psychological safety, ownership, and growth. Balance Innovation & Compliance: Deliver advanced solutions while meeting regulatory standards. Notice
Current Freddie Mac employees please apply through the internal career site. We are an equal opportunity employer. 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, disability, pregnancy, or any other protected status. Reasonable accommodations are available on request. Salary & Location
This position has an annualized market-based salary range and is eligible for incentive programs. The final salary offered will depend on responsibilities, experience, and qualifications. Washington, DC area and remote options may apply per policy.
#J-18808-Ljbffr