Role overview
- Technical Proficiency: Demonstrate deep expertise in data science, LLMs, and Agentic AI. Your ability to design and implement scalable solutions will be essential.
- Quality and Compliance: Establish and enforce evaluation frameworks and procedures to ensure production-ready deployment of AI/ML solutions.
- Collaboration: Work effectively with AI engineers, software engineers, product managers, and business stakeholders to build comprehensive AI experiences. Strong communication skills and a team-oriented mindset are key.
- Innovation and Problem-Solving: Employ modern AI/ML and GenAI design patterns and best practices to solve complex business problems. Your innovative approach will help integrate advanced AI into application architectures successfully.
- Agility and Adaptability: Thrive in a fast-paced, agile environment. Your flexibility and ability to adapt to new challenges and technologies will ensure the continuous improvement of our AI solutions.
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 www.BountyJobs.com and register with our referral code: MAC.
Time-type:Full timeFLSA 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 $144,000 - $216,000 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.
What you'll work on
- 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 we're looking for
- 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.