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.
Requirements:
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 robust integrations with third-party platforms, external APIs, and enterprise systems.
Full-Stack and Backend Engineering: Strong software engineering fundamentals and the ability to design and implement production-grade services, APIs, integrations, and supporting application components. Experience delivering maintainable, scalable solutions in complex technical environments.
Infrastructure and Reliability Engineering: Strong understanding of production system failure modes, including timeouts, concurrency issues, malformed requests, retry behavior, and service degradation. Ability to design resilient systems with appropriate monitoring, error handling, and recovery mechanisms.
Measurement-Driven Development: A rigorous, evidence-based approach to engineering, including defining measurable success criteria, building evaluation tools, analyzing system behavior, and using data to improve accuracy, performance, and reliability.
Cloud and Distributed Systems: Experience operating services in modern cloud environments and understanding the infrastructure required to support scalable, distributed applications.
Problem-Solving and Adaptability: Comfort working with ambiguous requirements and complex, specialized domains. Ability to investigate unfamiliar systems, learn new business processes quickly, and make sound technical decisions without relying on established playbooks.
Technical Communication and Collaboration: Strong communication skills and the ability to collaborate effectively with engineering peers, technical administrators, and operational stakeholders. Comfortable explaining architectural decisions, identifying trade-offs, and aligning teams around implementation plans.
Technical Leadership: Ability to take ownership of complex initiatives, establish engineering best practices, contribute to design and code reviews, and mentor other engineers while remaining a hands-on individual contributor.
Regulated Industry Awareness: Mortgage industry experience is not required, but an interest in learning mortgage fulfillment workflows and applicable regulatory requirements, including TRID and RESPA, is valuable.
Benefits:
Competitive Base Salary: Annual base salary varies by geographic location:
$164,400–$262,600 in California, Connecticut, Maryland, Massachusetts, New Jersey, New York, Washington state, and Washington, D.C.
$156,200–$249,400 in Colorado, Hawaii, Illinois, Maine, Minnesota, Nevada, Ohio, Rhode Island, Vermont, and Virginia.
Salary ranges for other U.S. locations may differ. Actual compensation depends on experience, performance, and location.
Equity Awards: Eligibility for equity compensation in addition to base salary, with awards determined by factors such as experience, performance, and location.
Fully Remote Work: Full-time remote position for eligible U.S.-based candidates, with flexibility to work from an approved location within the United States. Certain geographic restrictions may apply.
Flexible Working Environment: A distributed work model designed to support autonomy, collaboration, and productivity.
Technical Innovation: Opportunities to work on production-grade agentic AI, LLM infrastructure, evaluation systems, and intelligent automation in the mortgage technology space.
Professional Growth: Opportunities to deepen expertise in AI engineering, distributed systems, cloud infrastructure, and regulated financial technology.
Technical Leadership Opportunities: Influence architecture decisions, establish engineering standards, and mentor colleagues while remaining actively involved in hands-on development.
Collaborative Team Culture: Work closely with engineering, platform, and mortgage operations specialists in a team that values thoughtful technical decisions, reliability, and continuous improvement.
Equal Employment Opportunity: A commitment to an inclusive workplace that welcomes qualified candidates from diverse backgrounds.
Accessibility Support: Reasonable accommodations are available for qualified candidates with disabilities or other accessibility needs during the application and interview process.