Freddie Mac is seeking a Gen AI Engineering Manager to architect and deliver next-generation GenAI applications, agentic workflows, and AI-powered platforms to address diverse and complex business use cases and transform its Single-Family Acquisitions business.
Requirements
- Deep expertise in prompt engineering, fine-tuning, RAG, Graph RAG, vector databases (AWS Knowledgebase, Elastic), and multi-modal models.
- Proven experience with cloud-native AI development (AWS SageMaker, Bedrock, MLFlow, Kubeflow on EKS).
- Strong programming skills in Python and ML libraries (Transformers, LangChain, etc.).
- Deep understanding of Gen AI 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.
- Experience in regulated financial environments with compliance automation.
- Prior work implementing agentic workflows and AI-powered enterprise platforms.
Responsibilities
- Architect and implement scalable AI agents, agentic workflows, and GenAI applications tailored for Freddie Mac’s most complex business challenges.
- Develop, fine-tune, and optimize lightweight LLMs; lead the evaluation and adaptation of models such as Claude (Anthropic), Azure OpenAI, and open-source alternatives.
- Design and deploy Retrieval-Augmented Generation (RAG) and GraphRAG solutions using vector databases and enterprise knowledge bases (e.g., AWS Bedrock Knowledge Base, Elastic).
- Curate enterprise data using connectors integrated with AWS Bedrock's Knowledge Base/Elastic to support robust knowledge retrieval.
- Implement solutions leveraging Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication patterns.
- Build and maintain Jupyter-based notebooks using platforms such as SageMaker and MLFlow/Kubeflow on Kubernetes (EKS).
- Design and build robust ingestion pipelines to extract, chunk, enrich, and anonymize data from PDFs, video, and audio for LLM-powered workflows, leveraging semantic chunking and privacy best practices.
Other
- Lead a cross-functional team of data scientists and full stack engineers.
- Champion flawless execution, drive strategic innovation, master stakeholder engagement, and build an agentic, high-performance engineering culture.
- Collaborate with business stakeholders to identify and incubate innovative ideas.
- Establish agile, empirically driven SDLC and manage delivery metrics.
- Identify and champion technology-driven opportunities (GenAI, ML, cloud, data platforms).
