This practice is built for the way enterprise software actually gets made now: small senior pods, AI agents doing much of the build, and outcomes in weeks where traditional delivery approaches took months. We build digital products, AI agents, intelligent workflows and modernized platforms that help organizations operate differently across healthcare and life sciences, manufacturing, energy and utilities, transportation and consumer industries. None of it is pilots or demos. The solutions we build run in production and solve problems that matter to our clients' businesses.
As an AI Architect, you'll work directly with clients to shape, design, and lead the delivery of those solutions. Working alongside Product Strategists and AI Engineers, you'll own the delivery architecture for complex engagements, translating business problems into executable solutions and ensuring what gets built remains aligned to what was designed. You'll help shape opportunities before delivery begins and stay close to them throughout implementation. On some engagements, we continue to run what we've built, giving you the opportunity to stay close to the product and see the impact of your work long after go-live.
This is a hands-on architecture and engineering role. Most of the code here is written by agents. That does not make this a supervision job. It moves the engineering upstream, where strong engineering judgment becomes even more important. You'll define the architecture, controls, and acceptance criteria that determine whether a solution succeeds in production, while helping teams navigate ambiguity and move delivery forward. You'll be expected to make technical decisions that stand up not only in delivery, but also in front of client technology leaders who will ultimately be accountable for the outcome. Requirements do not always arrive fully formed, and part of the role is helping define the problem while continuing to move delivery forward.
As part of the Digital Products & AI team, you can expect to:Own the delivery architecture for digital products, AI agents, intelligent workflows, and modernization initiatives, ensuring solutions remain aligned from discovery through deployment.Architect the elements that determine whether AI solutions succeed in production, including tool selection, memory and context management, guardrail design, and human-in-the-loop controls.Define, build, and guide the implementation of the technical components that power agentic systems, including retrieval pipelines, MCP services and integrations, and the systems that connect AI capabilities to enterprise data and processes at scale.Modernize platforms and applications around current business needs, rebuilding where necessary rather than simply moving legacy functionality onto newer technology.Design security and operational resilience into solutions from the start, including access controls, identity management, deployment guardrails, and oversight of automated actions.Break down complex problems into deliverable components, defining scope, sequencing, implementation approaches, and delivery plans while determining feasibility and technical risk.Create reusable patterns, approaches, and tooling that strengthen future engagements, with the strongest ideas becoming capabilities and products we take to market.
