AI Engineering Lead

We are seeking a hands-on AI Engineering Lead / AI SDLC Architect to help accelerate the adoption of AI across our software development organization. This role will be responsible for designing and building the technical frameworks, coding harnesses, RAG capabilities, agent-based solutions, and reusable components that allow development teams to incorporate AI directly into the software development lifecycle.

The ideal candidate combines strong AI and software engineering expertise with the maturity and influence required to work across multiple teams, navigate competing viewpoints, and move initiatives forward even when the path is not clearly defined.

Responsibility Overview

• Lead the design and implementation of AI-enabled development capabilities, including coding harnesses, RAG solutions, AI agents, and agentic workflows.
• Build coding harnesses and reusable AI development frameworks that enable engineering teams to incorporate AI into their day-to-day development processes.
• Help establish scalable AI engineering standards and patterns that allow teams to accelerate delivery without sacrificing quality, security, or maintainability.
• Drive RAG capabilities end to end, from requirements and architecture through development, integration, testing, deployment, and production readiness.
• Design solutions at an architectural level while remaining hands-on, including building prototypes, integrations, frameworks, and production-ready components when needed.
• Establish and enhance AI SDLC practices, incorporating AI into requirements, development, testing, code quality, and delivery workflows.
• Create plug-and-play AI components and patterns that can be adopted and reused by multiple engineering teams across the line of business.
• Partner with EQE and engineering teams to establish appropriate testing, validation, quality, and production-readiness practices for AI solutions.
• Serve as a close technical partner to the Lead Developer, to implement AI capabilities across the development teams
• Coordinate across engineering, architecture, AI, platform, and other teams to bring the necessary AI SDLC expertise into the organization.
• Operate effectively in situations where ownership or direction may not be completely defined identify the solution, bring the right teams together, or implement it directly when necessary.
• Facilitate technical discussions and constructively challenge proposed solutions while remaining open to alternative approaches and evidence.
• Navigate competing priorities and stakeholder perspectives professionally while maintaining strong cross-functional relationships.

Must-Have Requirements

• Strong hands-on experience designing and implementing Generative AI and AI-assisted software development solutions.
• Practical experience implementing Retrieval-Augmented Generation (RAG) solutions end to end.
• Experience designing or implementing AI agents and agentic workflows.
• Experience building coding harnesses, AI development frameworks, or similar tooling that enables engineering teams to incorporate AI into their development workflows.
• Strong understanding of AI-enabled SDLC (AI SDLC) practices and the ability to integrate AI into requirements, development, testing, and delivery processes.
• Ability to take a business or technical requirement and determine how AI should process it, what context/data is required, which systems must be accessed, and how the complete workflow should be orchestrated.
• Strong software engineering background with the ability to remain hands-on with implementation, prototypes, integrations, and technical problem solving.
• Architecture-level experience designing CI/CD pipelines, integration patterns, reusable frameworks, and enterprise-scale solutions.
• Ability to build plug-and-play components that can be adopted and reused by multiple development teams across a line of business.
• Experience translating requirements into user stories, technical designs, development tasks, and executable delivery plans.
• Strong understanding of software quality, testing/EQE, security, governance, and production-readiness requirements.
• Demonstrated ability to work across development, architecture, AI, platform, and business teams to drive an initiative from concept through production.
• Strong communication and influencing skills, including the confidence to challenge technical decisions constructively and advocate for a solution when appropriate.
• Professional maturity and resilience—the ability to receive technical pushback, accept when another solution is better, and continue moving the team toward the best outcome without allowing disagreement to become personal.
• Ability to operate successfully in an environment with competing priorities and stakeholders while maintaining productive working relationships.