Senior Fullstack Engineer, Mortgage Fulfillment AI

Accountabilities:

• AI Agent Design and Architecture: Design, develop, and deploy sophisticated multi-step AI agents that coordinate mortgage fulfillment workflows, including document collection, loan condition clearing, and closing coordination. Build systems that automate repetitive processes while escalating decisions requiring human judgment.

• Evaluation Frameworks and AI Reliability: Develop and maintain evaluation pipelines that measure agent accuracy, reliability, and task completion. Establish clear success criteria, create and manage labeled datasets at scale, and use evaluation results to improve prompts, model selection, workflow logic, and fallback mechanisms.

• Agent Infrastructure and Orchestration: Architect and operate the core infrastructure supporting AI agents, including model serving, prompt pipelines, batching, queueing, and workflow orchestration. Design resilient systems with idempotent operations, retry mechanisms, graceful degradation, and structured logging to support dependable production execution.

• Third-Party Systems Integration: Build and maintain reliable integrations with external platforms and APIs, including mortgage loan origination and document management systems such as Encompass and DocuTech. Work within vendor constraints and supported extension points to retrieve system state, execute actions, and maintain data consistency.

• Production Engineering and Operations: Monitor and optimize AI systems for performance, availability, cost, and correctness. Identify and resolve production issues involving timeouts, race conditions, malformed payloads, retry storms, and other operational failures that can affect agent execution.

• Cross-Functional Collaboration: Partner with mortgage fulfillment operations, platform engineering teams, and loan origination system administrators to clarify requirements, evaluate technical constraints, and deliver improvements iteratively. Translate operational challenges into practical engineering solutions.

• Technical Leadership and Architecture Ownership: Own technical workstreams from initial design through deployment, measurement, and ongoing improvement. Make informed architectural decisions, surface trade-offs, manage technical risks, and establish clear implementation plans in an evolving problem space.

• Engineering Standards and Best Practices: Promote strong practices in automated testing, system measurement, observability, incident response, and production readiness. Contribute to architecture and code reviews, document technical decisions, and mentor engineers through collaborative problem-solving.

• Continuous Improvement: Use production data, evaluation results, and operational feedback to identify opportunities to improve agent behavior, system efficiency, and workflow automation. Balance rapid experimentation with the reliability and governance required in a regulated financial services environment.

• Professional Experience: At least eight years of professional software engineering experience, including building, deploying, and operating scalable, reliable services in modern cloud environments.

• Education: Bachelor's degree in Computer Science, Computer Engineering, or a related technical discipline, or an equivalent combination of education and professional experience.

• Production AI/ML Ownership: Demonstrated experience owning an AI or machine learning system end to end in production, including architecture, implementation, deployment, measurement, and iterative improvement. Ability to take accountability for correctness, operating costs, and system reliability.

• Specialized AI and Infrastructure Expertise: Hands-on experience in at least two of the following areas:

• Designing and operating AI evaluation pipelines, including generating and managing labeled datasets at scale.

• Deploying and operating large language model (LLM) or machine learning model-serving infrastructure and orchestrating multi-step AI workflows.

• Building