Full Stack AI Software Engineer

About the role

The Full-Stack AI Software Engineer will play a pivotal role in designing, building, deploying, securing, and continuously improving enterprise software and AI-enabled applications.

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.
• 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.

AI-Native Engineering

• 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.
• 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.

Software Delivery and Testing

• 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.

Application Ownership and Operational Excellence

• 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.

Security, Architecture, Data, and Support Collaboration

• 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.
• Coordinate with infrastructure, cloud, network, identity, DevOps, quality-assurance, product, legal, risk, and compliance teams as required.

Engineering Standards and Governance

• 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.

Other Job Duties and Responsibilities

• Performs other related duties as assigned.
• Comply with all company policies and procedures.
• Maintain regular and punctual attendance.

Qualifications

• 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.
• Able to independently manage multiple priorities and maintain ownership of up to three applications.