Data Scientist / ML Engineer Gen AI

Freddie Mac

13h

About The Position

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. Continue your career journey where your work contributes to a greater purpose.
Position Overview:
Freddie Mac is seeking a hands-on Data Scientist / ML Engineer to lead the design, development, and deployment of innovative AI/ML, Generative AI (GenAI), and Agentic AI solutions for Enterprise Risk organization. This role requires advanced proficiency in data science, data engineering, and expertise in LLMs/GenAI to build scalable, production-ready systems that drive business impact. You will work across the entire AI lifecycle, from data collection and model experimentation to production deployment and monitoring, collaborating with cross-functional teams to deliver enterprise-grade AI solutions.

Requirements

• Bachelor's or equivalent experience; advanced studies/degree preferred. Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field (or equivalent practical experience).
• 5+ years of experience in designing and deploying production-grade AI/ML solutions, including at least one production LLM agent or Agentic workflow.
• Deep expertise in Python and SQL, with 3+ years of experience in data science and AI/ML frameworks (scikit-learn, TensorFlow, PyTorch).
• 3+ years of proven experience with cloud-native development and data warehousing solutions (Snowflake, Azure Data Lake, AWS S3).
• Strong knowledge of LLMs, transformers, NLP, agentic modeling, and reinforcement learning concepts.
• 2+ years of experience with open and commercial LLMs, RAG pipelines, and agent frameworks (LangGraph/LangChain Agents, DSPy, or equivalent).
• Expertise in building data ETL/ELT pipelines and deploying models/LLMs using Docker/Kubernetes, CI/CD, and monitoring/observability tools.
• Excellent analytical, problem-solving, and critical-thinking skills.
• Demonstrated ability to work in cross-functional agile teams.

Responsibilities

• End-to-End AI/ML Solution Delivery: Own the full lifecycle of AI/ML projects, including problem framing, data acquisition, feature engineering, model/LLM selection and fine-tuning, evaluation, deployment, monitoring, and continuous improvement.
• Data Engineering and Pipelines: Design, build, and maintain robust, scalable data pipelines for ingestion, preprocessing, and feature engineering, supporting both structured and unstructured enterprise data.
• LLM/GenAI & Agentic AI: Implement RAG pipelines using vector databases and embedding strategies to ground LLMs in proprietary enterprise data; fine-tune, prompt-engineer, and evaluate LLMs for domain-specific tasks; design and orchestrate Agentic workflows including tool-using agents, multi-step planners, guardrails, and alignment mechanisms.
• Trustworthy AI & Risk Controls: Establish robust evaluation frameworks (hallucination checks, calibration, bias/fairness, adversarial tests), logging/telemetry, safeguards, governance artifacts, and documentation for model risk management.
• MLOps and Deployment: Lead the end-to-end lifecycle of AI models, from experimentation and prototyping to scalable deployment in production environments using MLOps best practices, CI/CD, and cloud platforms (AWS, Azure, GCP).
• Performance Monitoring and Optimization: Optimize inference latency, throughput, and cost; establish monitoring and observability to ensure performance, safety, and reliability in mission-critical environments.
• Collaboration and Strategy: Work closely with AI engineers, software engineers, product managers, and business stakeholders to translate complex business problems into AI-native solutions with measurable impact.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level