AI Mortgage Operations Solutions Engineer

About the role

We are seeking an AI Mortgage Operations Solutions Engineer to work directly with Consumer Direct Mortgage Fulfillment and LOS operations leadership.

Responsibilities

• Partner directly with Consumer Direct Mortgage Fulfillment and LOS operations leaders to understand workflows, pain points, capacity constraints, quality issues, exception patterns, manual handoffs, and opportunities for AI-enabled automation.
• Operate in a forward-deployed model, rapidly converting business requirements, process observations, and SME feedback into working prototypes, pilot solutions, and production-ready capabilities.
• Evaluate whether each workflow should be solved through deterministic automation, AI-assisted agentic workflow, human review, custom software development, or a hybrid approach.
• Prioritize approved LOS-native workflow, automation, agentic task, permissioning, rules, document handling, and integration capabilities before building external solutions.
• Design, configure, develop, test, and deploy AI-enabled task flows that combine deterministic automation, AI-assisted interpretation, human review, escalation routing, audit trails, and operational monitoring.
• Define agentic workflow objectives, entry criteria, completion criteria, required evidence, blocking conditions, permission boundaries, escalation paths, human approval points, test cases, rollout criteria, and success measures.
• Configure or support configuration of workflow personas, role-based permissions, document access, loan-data access, integration authorizations, and least-privilege controls where platform-native capabilities are used.
• Translate business requirements into functional solution designs, including workflows, prompts, tools, data inputs, decision logic, user interaction patterns, permission models, audit requirements, and escalation paths.
• Develop custom applications, APIs, workflow utilities, dashboards, integrations, and lightweight services when approved native platform capabilities are not sufficient to meet the business need.
• Integrate AI-enabled solutions with approved enterprise systems, loan origination platforms, document repositories, communication tools, reporting platforms, workflow queues, and third-party services.
• Deliver solutions through approved SDLC practices, including requirements definition, technical design, secure development, peer review, testing, release planning, deployment, documentation, monitoring, and support transition.
• Build solutions that meet enterprise engineering standards for maintainability, security, scalability, observability, reliability, automated testing, version control, and production support.
• Ensure rapid prototypes are evaluated for production readiness before broader deployment, including architecture review, information security review, data access review, model or AI governance review, operational support planning, and rollback or disablement procedures.
• Design human-in-the-loop review processes, escalation triggers, exception handling, audit evidence, monitoring routines, and governance controls for AI-enabled workflows used in mortgage operations.
• Document solution design, intended use, data sources, business rules, permissions, limitations, testing results, deployment approach, support model, and ongoing monitoring requirements.
• Partner with training and operations leaders to prepare user guidance, adoption plans, feedback loops, measurement plans, and change-management materials.
• Track and communicate value delivered, including cycle-time reduction, productivity improvement, reduced rework, improved consistency, improved operational visibility, reduced manual handoffs, and stronger exception management.
• Use approved enterprise AI, cloud, automation, workflow, and integration platforms as appropriate, while prioritizing native LOS workflow and automation capabilities where they provide the safest, fastest, and most supportable path to value.
• Stay current on emerging AI agent, workflow orchestration, responsible AI, automation, cloud-native, and mortgage operations technology practices.
• Other duties as assigned by leadership.

Qualifications

• 3+ years of experience in software development, automation engineering, AI application development, data engineering, workflow engineering, solutions engineering, or a related technical role.
• Hands-on experience building with large language models, AI agents, copilots, workflow automation, API-based applications, or AI-enabled business process solutions.
• Strong software engineering fundamentals, including modular design, object-oriented or functional programming concepts, API design, data modeling, source control, automated testing, debugging, observability, secure coding, and maintainable code practices.
• Experience delivering software through a structured SDLC, including requirements clarification, design, development, code review, testing, release management, documentation, production deployment, monitoring, incident response, and ongoing support.
• Ability to build production-quality solutions, not just prototypes, with appropriate error handling, logging, configuration management, access control, monitoring, scalability, resiliency, and supportability.
• Experience working directly with business users, operations leaders, or process owners to translate ambiguous operational needs into deployable technical solutions.
• Experience designing workflow-centric solutions involving task routing, exception handling, decision support, user interaction, system integration, and operational monitoring.
• Strong understanding of prompt engineering, retrieval-augmented generation, tool or function calling, structured outputs, agent orchestration, workflow design, and human-in-the-loop patterns.
• Experience integrating applications with APIs, databases, document repositories, workflow platforms, reporting tools, enterprise applications, or business systems.
• Proficiency in one or more programming languages such as Python, JavaScript, TypeScript, C#, Java, or a comparable modern language.
• Experience evaluating whether a business process should be solved through native platform configuration, workflow automation, AI-assisted reasoning, human review, or custom software development.
• Understanding of role-based access, permission models, least-privilege design, audit trails, escalation workflows, testing controls, and production-readiness requirements.
• Strong problem-solving skills with a bias toward practical delivery, rapid prototyping, iterative feedback, measurable business impact, and production supportability.
• Excellent communication skills, including the ability to work directly with non-technical operations leaders and translate technical concepts into business terms.
• Ability to work effectively in a regulated environment where data privacy, auditability, consistency, borrower impact, quality, compliance, and operational controls are critical.
• Ability to work across approved enterprise platforms and cloud ecosystems without being tied to a single vendor stack.

Preferred Qualifications

• Experience in mortgage banking, loan origination, consumer direct lending, fulfillment, processing, underwriting support, condition management, closing, servicing, financial services, or another regulated operating environment.
• Familiarity with loan origination systems, lending workflow platforms, document management systems, fulfillment queues, third-party mortgage services, and common mortgage operations workflows.
• Experience in a forward-deployed engineering, solutions engineering, technical product, business automation, operational transformation, or technology consulting role.
• Experience configuring or extending enterprise platforms using native workflow, automation, rules, permissions, integrations, and user-facing configuration capabilities.
• Experience with enterprise AI, cloud, automation, and workflow platforms across one or more major ecosystems, such as AWS, Azure, Google Cloud, OpenAI-compatible services, enterprise low-code/no-code platforms, workflow automation platforms, business intelligence tools, document repositories, collaboration platforms, or similar technologies.
• Experience building AI-enabled applications or workflow automations using cloud-native services, serverless functions, API gateways, event-driven architectures, managed AI services, orchestration tools, or enterprise integration platforms.
• Experience with agentic AI frameworks, orchestration tools, workflow engines, or automation platforms such as Semantic Kernel, LangChain, LangGraph, AutoGen, CrewAI, AWS Step Functions, Azure Logic Apps, Power Platform, or comparable technologies.
• Experience with retrieval-augmented generation, vector databases, knowledge management systems, document AI, OCR, intelligent document processing, or evidence-backed decision-support workflows.
• Experience deploying AI-enabled prototypes that evolve into production solutions.