Staff Engineer/AI Solutions Architect

We are assisting a client, a mortgage technology solutions company, in their search for a hands-on Staff Engineer/AI Solutions Architect who can assess the current landscape, identify high-value AI opportunities, develop a practical implementation roadmap, and guide engineering teams through execution. This individual will play a central role in defining and executing the AI strategy while helping evolve broader technology architecture. The ideal candidate combines strategic thinking with strong technical depth and is comfortable moving between executive-level discussions, architecture decisions, and hands-on implementation. This position requires extensive experience in mortgage technology, modern application architecture, AWS, and enterprise AI implementation. This is an ideal opportunity for a strategic, hands-on technology leader who wants to shape the future of AI within a growing mortgage technology organization while solving complex data and workflow challenges across the mortgage capital markets.

Qualifications:

• 12-15+ years of overall software engineering, architecture, and implementation experience.
• At least 2 3 years of recent, hands-on experience implementing AI solutions.
• Strong mortgage technology and domain experience, preferably involving capital markets, secondary-market transactions, loan review, mortgage data, due diligence, risk management, or related workflows.
• Deep hands-on expertise with:
• Java
• Spring Boot
• AWS
• REST APIs and distributed systems
• Cloud-native and microservices architecture
• AI, machine learning, generative AI, or large language model integration
• Experience designing and deploying secure, scalable enterprise applications in AWS.
• Practical experience integrating AI models and services into existing business applications and workflows.
• Experience with AI solution patterns such as retrieval-augmented generation, vector search, intelligent document processing, prompt orchestration, model evaluation, and AI observability.
• Strong understanding of enterprise data architecture, data quality, integration, normalization, and governance.
• Ability to evaluate an existing technology landscape and create a pragmatic, phased modernization and AI roadmap.
• Demonstrated ability to remain hands-on while providing strategic and architectural leadership.
• Strong communication, collaboration, facilitation, and stakeholder-management skills.

Responsibilities:

AI Strategy and Implementation:

• Evaluate the client's technology, data, products, and operational workflows to identify opportunities where AI can deliver measurable business value.
• Define and guide an enterprise AI strategy aligned with the client's capital markets products and long-term technology roadmap.
• Develop a prioritized AI implementation roadmap covering near-term use cases and longer-term platform capabilities.
• Lead hands-on implementation of AI solutions, prototypes, proofs of concept, and production-ready services.
• Identify opportunities involving generative AI, large language models, intelligent document processing, data extraction, workflow automation, predictive analytics, and decision-support capabilities.
• Establish appropriate patterns for retrieval-augmented generation, model integration, prompt management, evaluation, monitoring, and human oversight.
• Evaluate third-party AI products, foundation models, cloud services, and internal development options.
• Define AI governance and engineering guardrails related to security, privacy, explainability, accuracy, model risk, fair lending, regulatory compliance, and responsible AI use.

Architecture and Engineering Leadership:

• Assess the existing application, integration, cloud, and data landscape and recommend scalable architectural improvements.
• Define solution architecture across Java, Spring Boot, Angular, AWS, data platforms, APIs, and AI services.
• Establish reusable architectural patterns and standards for integrating AI capabilities into existing products and workflows.
• Provide technical direction and hands-on guidance to engineering teams throughout design, development, testing, and deployment.
• Review designs and code to ensure solutions meet requirements for scalability, security, performance, resiliency, and maintainability.
• Partner with product, engineering, data, security, compliance, and business stakeholders to translate strategic objectives into executable technology initiatives.
• Communicate technical strategy, architectural decisions, risks, and tradeoffs clearly to both technical and nontechnical stakeholders.

Mortgage and Capital Markets Solutions:

• Apply deep mortgage-domain expertise to the design of AI-enabled technology solutions.
• Identify AI opportunities across mortgage data aggregation, document review, loan-level analysis, due diligence, risk management, quality control, exception management, and reporting.
• Help ensure that solutions reflect mortgage-industry workflows, data standards, investor requirements, regulatory considerations, and secondary-market practices.