Automation Engineering, Tech Lead at Freddie Mac

Job description

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. Join an organization where your work contributes to a greater purpose. Position Overview We are seeking a highly experienced Senior Software Developer / Technical Lead in Automation Engineering and AI Solutions Development to design, build, and maintain scalable Python applications that power our in-house agentic GenAI solutions. This is primarily a hands-on software engineering role. You will own the end-to-end development of robust backend services, APIs, and automation frameworks, applying strong software engineering fundamentals to AI-enabled systems. You will lead the development of production-grade GenAI solutions leveraging technologies such as LLMs, RAG architectures, and vector databases within modern cloud-native frameworks while ensuring scalability, reliability, and responsible AI practices. Our Impact At Freddie Mac, our mission of Making Home Possible drives everything we do. As we advance into an AI-powered future, we are transforming how risk management is designed, implemented, and scaled — moving from manual processes to intelligent, automated, and embedded risk capabilities. This role is part of a next-generation initiative to reimagine risk management through GenAI and automation, where controls are engineered into systems, not just documented. Your Impact In this role, you will lead the design, build, and maintenance of Python-based microservices, APIs, and backend services that power scalable GenAI applications, AI agents, and agentic workflows. You will lead automation framework design, build and optimize RAG pipelines and vector search solutions, and ensure efficient deployment, high system performance, and well-tested, maintainable code. Design, build, and operate MCP (Model Context Protocol) servers that serve as the backbone of our agentic AI solutions encapsulating internal logic including Panel of Experts orchestration, Judge-Draft-Refine loops, RAG retrieval, and tool-calling integrations. Design and implement multi-agent systems, including Lead Agent and Reviewer Agent patterns, consensus mechanisms, and iterative refinement loops that ensure quality, accuracy, and auditability of AI-generated outputs. Create data pipelines and feature engineering workflows, establish feedback loops for continuous improvement, and integrate AI capabilities into production systems. As part of building these systems responsibly, you will help embed risk and control mechanisms into GenAI workflows and partner with Risk, Security, and Governance teams to translate requirements into reliable code. Build observable and auditable AI systems with structured metadata, run-level logging, traceability, and audit trails ensuring outputs are explainable and traceable in a regulated environment. Establish Python project standards, scaffolding templates, and development best practices that accelerate team onboarding and reduce setup friction. Collaborate across engineering, product, risk, security, and governance teams; provide technical leadership grounded in engineering excellence; and communicate complex issues, decisions, and recommendations clearly to both technical and executive audiences. Proactively identify and raise technical, delivery, risk, and dependency concerns; lead escalations through resolution; and communicate issues, tradeoffs, and recommended actions clearly and concisely to leadership. Qualifications Bachelor’s degree in computer science, Engineering, or a related field (advanced degree preferred) 8–10 years of experience in software engineering Strong proficiency in Python, including experience building production-grade APIs, microservices, automation frameworks, and backend services Experience building AI-enabled applications, automation platforms, or intelligent software solutions Experience integrating LLMs and AI services into business applications, including familiarity with RAG, prompt engineering, or similar AI patterns Experience with MLOps/DataOps pipelines, observability, and monitoring tools Familiarity with vector databases and modern AI architectures Experience with platforms such as AWS Bedrock, Azure OpenAI, and GitHub Copilot Experience with LLM evaluation, guardrails, or AI safety mechanisms Familiarity with AI governance, model risk, or responsible AI frameworks Experience implementing risk, control, or compliance requirements in technical systems Experience building or working with MCP (Model Context Protocol) servers or equivalent tool-calling and agent-serving frameworks Experience designing or building multi-agent systems, including agent orchestration, Lead Agent / Reviewer Agent patterns, review loops, and consensus mechanisms Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar Experience building observable and auditable AI systems with structured metadata, logging, and traceability — particularly in regulated, risk, or compliance environments Experience preparing and curating data for RAG ingestion, including chunking strategies, file format standardization, retrieval validation, and citation traceability Ability to establish Python project standards, scaffolding, and development best practices for an engineering team Preferred / Nice to Have Familiarity with enterprise AI platforms such as FredAI or similar internal LLM orchestration and MCP-enabled platforms Experience with Freddie Mac deployment processes Experience with distributed systems and cloud-native architectures (AWS preferred) Key to Success in the Role Technical Proficiency: Strong Python skills and ability to build scalable microservices and production-ready agentic systems Quality & Reliability: Deliver robust, well-tested, and production-ready solutions with full observability and audit support Learning Agility: Continuously develop skills in GenAI, agentic frameworks, and emerging AI technologies Collaboration: Work effectively across engineering, product, risk, security, and governance teams Attention to Detail: Ensure high-quality data, outputs, and system performance especially in regulated workflows Agent Engineering Mindset: Ability to design, build, and debug multi-agent systems including orchestration logic, tool integrations, and iterative refinement loops Platform Awareness: Understand and design within platform constraints (context windows, MCP limitations, memory management) and proactively document them as architecture inputs Leadership Communication & Escalation: Surface risks, delivery concerns, and dependencies early; manage escalations constructively and communicate complex issues, tradeoffs, and recommended actions clearly and confidently. 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 time FLSA 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 $146,000 - $218,000 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.

Requirements

Must-haves

This role is part of a next-generation initiative to reimagine risk management through GenAI and automation, where controls are engineered into systems, not just documented.
Design and implement multi-agent systems, including Lead Agent and Reviewer Agent patterns, consensus mechanisms, and iterative refinement loops that ensure quality, accuracy, and auditability of AI-generated outputs.
Build observable and auditable AI systems with structured metadata, run-level logging, traceability, and audit trails ensuring outputs are explainable and traceable in a regulated environment.
Establish Python project standards, scaffolding templates, and development best practices that accelerate team onboarding and reduce setup friction.
Proactively identify and raise technical, delivery, risk, and dependency concerns; lead escalations through resolution; and communicate issues, tradeoffs, and recommended actions clearly and concisely to leadership.
8–10 years of experience in software engineering
Strong proficiency in Python, including experience building production-grade APIs, microservices, automation frameworks, and backend services
Experience building AI-enabled applications, automation platforms, or intelligent software solutions
Experience integrating LLMs and AI services into business applications, including familiarity with RAG, prompt engineering, or similar AI patterns
Experience with MLOps/DataOps pipelines, observability, and monitoring tools
Familiarity with vector databases and modern AI architectures
Experience with platforms such as AWS Bedrock, Azure OpenAI, and GitHub Copilot
Experience with LLM evaluation, guardrails, or AI safety mechanisms
Familiarity with AI governance, model risk, or responsible AI frameworks
Experience implementing risk, control, or compliance requirements in technical systems
Experience building or working with MCP (Model Context Protocol) servers or equivalent tool-calling and agent-serving frameworks
Experience designing or building multi-agent systems, including agent orchestration, Lead Agent / Reviewer Agent patterns, review loops, and consensus mechanisms
Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar
Experience building observable and auditable AI systems with structured metadata, logging, and traceability — particularly in regulated, risk, or compliance environments
Experience preparing and curating data for RAG ingestion, including chunking strategies, file format standardization, retrieval validation, and citation traceability
Ability to establish Python project standards, scaffolding, and development best practices for an engineering team

Nice-to-haves

Qualifications Bachelor’s degree in computer science, Engineering, or a related field (advanced degree preferred)
Preferred / Nice to Have Familiarity with enterprise AI platforms such as FredAI or similar internal LLM orchestration and MCP-enabled platforms
Experience with distributed systems and cloud-native architectures (AWS preferred)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonawsazurellm

Platform: workday
Effort: long