Note: The job is a remote job and is open to candidates in USA. Freedom Mortgage is a mortgage lending company seeking a Full Stack AI Software Engineer to design, build, deploy, secure, and continuously improve enterprise software and AI-enabled applications. The role translates business needs into scalable full-stack solutions, develops AI capabilities using large language models and agentic workflows, owns applications throughout their lifecycle, and mentors other engineers.
Responsibilities
- Partner directly with business stakeholders to understand operating processes, identify root problems, define measurable outcomes, and engineer effective technical solutions
- Translate ambiguous business needs into clear functional requirements, technical designs, user stories, acceptance criteria, and production-ready software
- Work interactively with users through rapid prototyping, demonstrations, feedback sessions, and iterative releases
- Build new applications and enhance existing platforms across the frontend, backend, application programming interface, integration, data, and infrastructure layers
- Develop intuitive, accessible, responsive, and high-performing user experiences
- Design reusable services, components, APIs, workflows, and integration patterns
- Make thoughtful build-versus-buy and configuration-versus-custom-development recommendations
- Balance delivery speed with security, reliability, maintainability, scalability, and long-term architectural integrity
- Use modern AI engineering tools throughout the software development lifecycle to improve development speed, quality, testing, documentation, and maintainability
- Design and implement AI-enabled capabilities using large language models, retrieval-augmented generation, embeddings, vector search, structured outputs, tool calling, multimodal models, and agentic workflows
- Build AI agents that can securely interact with applications, APIs, databases, documents, and enterprise systems
- Develop effective prompts, system instructions, context-management strategies, tool definitions, workflows, and reusable AI skills
- Evaluate and select appropriate models based on accuracy, latency, security, cost, reliability, and business requirements
- Implement model evaluations, regression tests, guardrails, human-review controls, grounding, observability, and fallback mechanisms
- Identify and mitigate hallucination, prompt-injection, data-leakage, model-bias, unsafe-output, and unauthorized-tool-use risks
- Measure AI solutions using defined quality, accuracy, safety, performance, adoption, and business-value metrics
- Maintain awareness of rapidly evolving AI models, development tools, frameworks, security risks, and engineering practices
- Own software delivery from initial discovery and architecture through development, testing, deployment, production support, and continuous improvement
- Write clean, modular, testable, secure, and well-documented production code
- Develop and maintain unit, integration, API, user-interface, regression, performance, security, and end-to-end automated tests
- Use AI-assisted testing and automation tools while independently validating generated code and test results
- Participate in peer reviews, design reviews, architecture reviews, threat modeling, and release-readiness assessments
- Build and maintain continuous integration and continuous delivery pipelines
- Release software through safe, incremental, observable, and reversible deployment practices
- Ensure that functional requirements and acceptance criteria are demonstrably satisfied before production release
- Serve as the accountable engineer or technical owner for up to three assigned applications
- Maintain a clear understanding of each application’s architecture, business purpose, users, integrations, data flows, dependencies, risks, and operational requirements
- Monitor application availability, performance, capacity, security, errors, and user experience
- Diagnose and resolve production incidents, defects, integration failures, and performance issues
- Perform routine maintenance, framework and dependency upgrades, operating-system or platform updates, certificate renewals, configuration changes, and technical-debt remediation
- Patch application and infrastructure vulnerabilities within established remediation timelines
- Maintain current application documentation, support procedures, runbooks, architecture diagrams, data-flow diagrams, deployment instructions, and recovery procedures
- Participate in incident response, root-cause analysis, corrective-action planning, disaster-recovery testing, and business-continuity exercises
- Recommend modernization, re-platforming, consolidation, or retirement when an application no longer meets business or technical standards
- Work closely with the Information Security team to implement secure coding standards, identity and access controls, encryption, secrets management, vulnerability management, logging, monitoring, and regulatory controls
- Partner with Enterprise Architecture to ensure solutions align with approved technology standards, integration patterns, cloud strategies, and target-state architecture
- Collaborate with enterprise database and data-management teams on data models, storage patterns, data quality, lineage, retention, privacy, and performance
- Coordinate with IT Support and operations teams to ensure applications can be effectively monitored, supported, maintained, and recovered
- Work with infrastructure, cloud, network, identity, DevOps, quality-assurance, product, legal, risk, and compliance teams as required
- Clearly communicate technical decisions, constraints, risks, tradeoffs, progress, and recommendations to both technical and nontechnical audiences
- Follow established software-development lifecycle, change-management, security, architecture, data-governance, and release-management standards
- Ensure applications comply with applicable legal, regulatory, privacy, records-management, accessibility, and industry requirements
- Maintain traceability between business requirements, technical designs, code changes, tests, approvals, and releases
- Protect confidential, proprietary, customer, and regulated data throughout the development and production lifecycle
- Ensure AI-generated code and content are reviewed, tested, licensed appropriately, and treated with the same rigor as human-created work
- Contribute reusable engineering patterns, libraries, templates, prompts, skills, development standards, and lessons learned to the broader engineering organization
- Mentor other engineers and help raise the organization’s standards for AI-enabled software delivery
- Performs other related duties as assigned
- Comply with all company policies and procedures
- Maintain regular and punctual attendance
Skills
- Five or more years of professional software-engineering experience across the design, development, testing, deployment, and support of production applications
- At least two to three years of professional experience in engineering software using modern AI-assisted engineering tools and AI development practices
- Demonstrated ability to build and support full-stack applications from concept through production
- Strong proficiency in at least one modern backend language, such as Python, Java, C#, Go, or TypeScript/Node.js
- Strong proficiency with modern frontend development using JavaScript or TypeScript and a framework such as React, Angular, or Vue
- Experience designing and consuming RESTful APIs, event-driven services, microservices, and enterprise integrations
- Experience with relational databases, SQL, data modeling, and at least one modern nonrelational or vector-database technology
- Practical experience integrating large language models or generative-AI services into production or enterprise applications
- Experience with AI coding tools such as Cursor, Claude Code, GitHub Copilot, OpenAI Codex, or comparable platforms
- Experience with Git-based source control, automated testing, code review, continuous integration, continuous delivery, and modern DevOps practices
- Experience deploying and operating applications in a major cloud environment such as AWS, Microsoft Azure, or Google Cloud Platform
- Working knowledge of containers, infrastructure as code, observability, application security, and vulnerability remediation
- Strong understanding of secure software-development principles, including authentication, authorization, encryption, secrets management, dependency management, and common application-security risks
- Demonstrated ability to work directly with business users, understand workflows, challenge assumptions constructively, and convert business problems into working software
- Strong analytical, troubleshooting, communication, collaboration, and documentation skills
- Ability to independently manage multiple priorities and maintain ownership of up to three applications
- Bachelor's or master's degree in computer science, software engineering, information systems, data science, artificial intelligence, or a related discipline, or equivalent practical experience
- Experience building AI agents, retrieval-augmented generation solutions, semantic-search applications, document-processing systems, or workflow automation
- Experience with model-evaluation frameworks, prompt testing, AI observability, red-team testing, and responsible-AI controls
- Experience with frameworks or platforms such as LangGraph, Semantic Kernel, OpenAI Agents SDK, model context protocol, or comparable orchestration technologies
- Experience with Kubernetes, serverless architectures, messaging platforms, API gateways, and distributed systems
- Experience working in a regulated industry such as financial services, mortgage lending, banking, insurance, healthcare, or government
- Understands how mortgage products, servicing operations, borrower experience, compliance, and technology intersect to create business value
- Can effectively communicate with mortgage business stakeholders without requiring extensive business translation
- Designs technology solutions that improve operational efficiency while maintaining compliance and reducing risk
- Familiarity with privacy, model risk, information security, records retention, accessibility, and regulatory compliance requirements
- Experience modernizing legacy applications and integrating them with cloud, data, and AI platforms
- Experience developing software in close collaboration with product designers, business operators, and end users
- Demonstrated ability to improve development velocity without compromising engineering quality or operational stability
Benefits
- Remote work arrangement
Company Overview
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