Staff Engineer/AI Solutions Architect

Responsibilities

• 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.
• 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.
• 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.

Requirements

• 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.
• 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.
• A strong understanding of enterprise data architecture, data quality, integration, normalization, and governance.
• The 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.

Skills

• Not specified.