Forward Deployed AI Engineer

Technology, Data & Digital · Software & Web Development · Software Engineering · Data Science · Artificial Intelligence

In short

This role is for a Forward Deployed AI Engineer who embeds within client enterprises to operationalize complex AI platforms, focusing on outcomes like time-to-value, adoption, reliability, and scalability. You will lead enterprise AI platform deployments, architect solutions across the full technology stack, and shape AI reinvention strategy for senior client leadership.

Responsibilities

Lead enterprise AI platform deployments across complex multi-stakeholder client environments, owning the full program from architecture through adoption.
Own program-level delivery outcomes: time-to-value, reliability, adoption velocity, and scalability across multiple concurrent workstreams, with commercial metrics attached.
Lead rapid experimentation at pace: drive ambiguous business problems to working production systems in days or weeks across complex enterprise environments.
Architect and govern enterprise AI solutions across the full technology stack: identity, data, security, governance, platform layer, and multi-system workflow integration at program scale.
Shape AI reinvention strategy for client CTO, CFO, and CISO: build value architecture, ROI backlogs, use case prioritization frameworks, and multi-year AI adoption roadmaps.
Define and publish reusable reinvention blueprints, patterns, and accelerators that scale across multiple client engagements and grow the FDE practice.
Lead architecture design sessions, executive workshops, and code-with sessions with client engineering and C-suite leadership teams.
Codify delivery learnings, failure patterns, and engineering standards that shape the FDE practice and enable the next generation of forward deployed engineers.

Requirements

Engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
Deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
Experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines.
Experience leading software engineering teams: overseeing delivery, allocating resources across workstreams, and owning the professional development of direct reports.
Demonstrated end-to-end delivery ownership in a client-embedded environment (internal projects, vendor labs, or team-only deployments do not qualify).
Proven ability to articulate business value: can quantify the impact of deployments in terms a CFO would recognize and act on.
Experience presenting to and building trust with senior client stakeholders, CTO, CFO, or CISO level.
People lead responsibilities: experience managing, developing, and performance-managing a team of engineers; setting individual development plans and conducting career conversations.

Desired Qualifications

Non-linear profiles are expected and welcomed, assessment is based on demonstrated deployment experience and outcome ownership, not CV pattern matching.

Benefits

Opportunities to keep skills relevant through certifications, learning, and diverse work experiences.
Support for people’s physical, mental, and financial health.
Work at the heart of change.
Consistently recognized as one of the World’s Best Workplaces™.

Skills

AIcloud-native systemsAPIsmicroservicescontainerizationserverlessagentic solutionsorchestrationcontext engineeringRAGworkflowsOpenAIClaudeVertex AIopen-source modelsabstraction layersmulti-provider pipelinessoftware engineeringarchitecturegovernanceidentitydatasecurityplatform layermulti-system workflow integrationvalue architectureROIuse case prioritizationAI adoption roadmaps

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