AI Integration Engineer

The AI Integration Engineer will provide hands‑on technical leadership for the architecture, integration, governance, and operationalization of enterprise AI engineering platforms. The role will drive AI‑enabled transformation across the end‑to‑end Software Development Lifecycle, integrating AI coding assistants, autonomous software engineering agents, Model Context Protocol (MCP) services, SaaS and on‑premises platforms, developer toolchains, DevSecOps automation, security controls, and production operations. The engineer will translate enterprise technology strategy, product objectives, risk requirements, and emerging AI capabilities into scalable, secure, resilient, and compliant engineering solutions while influencing architecture standards, integration patterns, engineering practices, and modernization roadmaps.

Key Responsibilities

• Lead the architecture and continued evolution of enterprise AI engineering capabilities across SaaS, cloud, desktop, and on‑premises environments.
• Design scalable, resilient, secure, and compliant integration patterns for AI coding assistants and software engineering agents.
• Define enterprise patterns for Model Context Protocol (MCP), remote and hosted MCP services, MCP gateways, enterprise tools, APIs, and platform interoperability.
• Provide hands‑on technical leadership for solution design, implementation, integration, and operationalization.
• Establish reusable architecture patterns, engineering standards, reference implementations, implementation playbooks, and platform guardrails.
• Lead high‑level architecture, end‑to‑end flow, authentication and authorization, network connectivity, API contract, and service integration design.
• Drive AI integration across the Define, Design, Develop, Test, Deploy, Release, and Operate phases of the SDLC.
• Enable integrations with requirements and collaboration platforms including Jira and Confluence.
• Enable design workflows and integrations with tools such as Figma and enterprise architecture services.
• Integrate AI capabilities with developer and software supply chain platforms including GitHub, GitHub Actions, Artifactory, Sonar, Checkmarx, and Black Duck.
• Integrate deployment and release workflows with Harness, Ansible, and ServiceNow.
• Enable operational integrations with observability and monitoring platforms such as Splunk and AppDynamics.
• Lead delivery from initiation and requirements through architecture and design, build and configuration, validation, security and governance, production readiness, and go‑live.
• Define functional and non‑functional requirements and perform tool capability assessments.
• Guide service account and secret configuration, router or gateway integration, proxy development, and connectivity with target services and tools.
• Lead non‑production deployment and connectivity, functional, integration, security, user acceptance, and performance testing.
• Initiate and support architecture, cybersecurity, third‑party or SaaS, risk, compliance, and governance reviews and approvals.
• Ensure solutions comply with enterprise security, data protection, technology risk, regulatory, and operational requirements.
• Drive change request initiation, production readiness reviews, and required change approvals.
• Establish monitoring, logging, alerting, incident response, support, and service management standards.
• Develop runbooks, playbooks, game plans, rollback strategies, and operational handoff models.
• Guide production deployment, post‑implementation testing, monitoring and alerting reviews, and continuous operational improvement.
• Partner with DevOps, Release Engineering, and Site Reliability Engineering teams to improve platform reliability, resilience, and supportability.
• Act as a trusted technical advisor to senior leadership on complex AI engineering, platform, application, infrastructure, security, governance, and software delivery decisions.
• Lead the strategy and resolution of enterprise challenges requiring evaluation across multiple technology areas and organizations.
• Maintain knowledge of industry practices and emerging technologies and recommend innovations that improve engineering productivity, delivery quality, operational effectiveness, or business outcomes.
• Partner with product owners, UX designers, developers, application architects, testers, DevOps engineers, release technology leads, release engineers, and site reliability engineers.
• Collaborate with Architecture, Cybersecurity, Infrastructure, Platform Engineering, Application Development, Quality Engineering, Risk, Compliance, and Operations teams.
• Facilitate architecture discussions and build alignment across product, engineering, governance, infrastructure, and operational stakeholders.
• Clearly communicate technical concepts, architecture decisions, risks, trade‑offs, and recommendations to technical and executive audiences.
• Mentor senior engineers and architects and contribute to enterprise communities of practice for AI‑enabled engineering.

Required Qualifications

• 7+ years of engineering experience, or equivalent demonstrated through work experience, training, military experience, or education.
• 5+ years of technical leadership experience driving enterprise‑scale engineering initiatives, architecture programs, platform integrations, or technology transformations.
• 5+ years of hands‑on experience in software engineering, platform engineering, DevOps, API integration, cloud engineering, or infrastructure automation.
• Experience designing and implementing enterprise‑grade solutions across modern SDLC, DevSecOps, CI/CD, cloud, SaaS, and on‑premises environments.
• Experience with enterprise architecture, APIs, service integration, authentication, authorization, secrets management, network connectivity, and security controls.
• Experience leading complex cross‑functional technology initiatives and influencing engineering, product, architecture, security, risk, and operations stakeholders.
• Experience establishing production readiness, observability, operational support, governance, and continuous improvement practices.
• Strong understanding of scalable, resilient, secure, and compliant enterprise integration architectures.

Preferred Qualifications

• Experience with Generative AI, Agentic AI, AI engineering platforms, Large Language Models, and AI‑assisted software engineering.
• Knowledge of Model Context Protocol (MCP), MCP gateways, agent orchestration, tool integration, and secure enterprise AI architecture patterns.
• Experience integrating AI‑powered developer productivity tools, coding assistants, or autonomous software engineering agents into enterprise workflows.
• Experience delivering enterprise technology solutions in a regulated financial services environment.
• Strong understanding of secure architecture, SaaS risk assessment, third‑party governance, data protection, technology risk, compliance, and operational controls.
• Demonstrated ability to create reusable frameworks, reference implementations, engineering standards, technical guidance, adoption roadmaps, and enablement programs.
• Ability to influence technical strategy and architecture decisions across multiple organizations and senior leadership teams.
• Excellent communication, stakeholder management, and executive presentation skills.