Embedded AI Deployment Lead

Accenture PLC; Greater London; On-site; GBP 90,000 - 130,000; Full time

Job summary

Accenture PLC is seeking a Forward Deployed AI Engineer who embeds within a client’s teams to deploy, scale, and operationalize AI platforms directly inside enterprise environments. You own outcomes such as time-to-value, adoption velocity, reliability, and scalability, not just milestones.

This role focuses on translating platform capabilities into measurable business value, shaping use case roadmaps, and building reusable patterns and accelerators for long-term client ownership.

Qualifications

• 3-5 years engineering experience with cloud-native systems (APIs, microservices, containerization, serverless).
• 1 year of deep expertise in designing and deploying agentic solutions in production environments.
• 3 years of experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models.
• 10 years of experience deploying to production, CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.

Responsibilities

• Embed with client teams to deploy, scale, and operationalize AI platforms inside enterprise environments.
• Own production outcomes end-to-end with time-to-value, reliability, adoption velocity, and scalability.
• Move from ambiguous problem to production-ready system with rapid prototyping.
• Design and govern AI architectures across identity, data, security, governance, and workflow integration.
• Translate architecture into business impact for client executives; shape use case roadmaps and ROI backlogs.
• Build reusable patterns, playbooks, and accelerators that persist with the client.
• Lead design workshops, proofs of concept, architecture walkthroughs, and code-with sessions.
• Codify delivery learnings to scale across engagements and grow the FDE practice.

Skills

Deployment ownership

Client stakeholder communication

Architecture design

Tools

Terraform

Helm

CI/CD

Job description

Accenture PLC is seeking a Forward Deployed AI Engineer who embeds within a client’s teams to deploy, scale, and operationalize AI platforms directly inside enterprise environments. You own outcomes such as time-to-value, adoption velocity, reliability, and scalability, not just milestones.

This role focuses on translating platform capabilities into measurable business value, shaping use case roadmaps, and building reusable patterns and accelerators for long-term client ownership.