Automation Engineering, Tech Lead

Freddie Mac•McLean, VA

6d•$146,000 - $218,000

About The Position

Freddie Mac is seeking a highly experienced Senior Software Developer / Technical Lead in Automation Engineering and AI Solutions Development. This primarily hands-on engineering role designs, builds, and maintains scalable Python applications that power in-house agentic GenAI solutions. The role owns end-to-end development of backend services, APIs, and automation frameworks, applying strong software engineering fundamentals to AI-enabled systems and leading production-grade GenAI solutions using LLMs, RAG architectures, vector databases, and modern cloud-native frameworks. The position contributes to a next-generation initiative to reimagine risk management through GenAI and automation, embedding controls into systems and supporting responsible AI practices. It leads engineering work across Python microservices, APIs, MCP servers, multi-agent systems, RAG pipelines, vector search, data pipelines, and AI integrations. It also establishes project standards and development practices, supports observable and auditable systems, and works with engineering, product, risk, security, and governance partners to translate requirements into reliable code. The role requires technical leadership, clear communication with technical and executive audiences, and proactive management of technical, delivery, risk, and dependency concerns. The position is full-time and exempt. The annualized market-based salary range is $146,000–$218,000, with eligibility to participate in the annual incentive program. Freddie Mac offers a comprehensive total rewards package; details of benefit programs are available on its Careers site.

Requirements

Bachelor’s degree in computer science, Engineering, or a related field.
8–10+ years of experience in software engineering.
Strong proficiency in Python, including 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 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.
Strong Python skills and ability to build scalable microservices and production-ready agentic systems.
Ability to deliver robust, well-tested, production-ready solutions with full observability and audit support.
Ability to continuously develop skills in GenAI, agentic frameworks, and emerging AI technologies.
Ability to work effectively across engineering, product, risk, security, and governance teams.
Attention to data quality, output quality, and system performance, especially in regulated workflows.
Ability to design, build, and debug multi-agent systems, including orchestration logic, tool integrations, and iterative refinement loops.
Ability to understand and design within platform constraints, including context windows, MCP limitations, and memory management, and to document them as architecture inputs.
Ability to surface risks, delivery concerns, and dependencies early, manage escalations constructively, and communicate complex issues and tradeoffs clearly and confidently.

Nice To Haves

Advanced degree preferred.
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.

Responsibilities

Design, build, and maintain Python-based microservices, APIs, and backend services for scalable GenAI applications, AI agents, and agentic workflows.
Design automation frameworks and build and optimize RAG pipelines and vector search solutions.
Design, build, and operate MCP servers that encapsulate 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.
Support efficient deployment and high system performance, and deliver well-tested, maintainable code.
Create data pipelines and feature engineering workflows, establish feedback loops for continuous improvement, and integrate AI capabilities into production systems.
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.
Establish Python project standards, scaffolding templates, and development best practices to accelerate onboarding and reduce setup friction.
Collaborate across engineering, product, risk, security, and governance teams and provide technical leadership grounded in engineering excellence.
Communicate complex issues, decisions, recommendations, risks, tradeoffs, and recommended actions clearly to technical and executive audiences and leadership.
Proactively identify and raise technical, delivery, risk, and dependency concerns, and lead escalations through resolution.