Forward Deployed Engineer AI-Managing Consultant-AI and Digital Factory

Location: Greater London

At Capgemini Invent, we believe difference drives change. As inventive transformation consultants, we blend our strategic, creative and scientific capabilities, collaborating closely with clients to deliver cutting-edge solutions. This role sits within our Applied AI Practice, at the sharp end of that mission: embedded directly with client teams, turning ambiguous problems into shipped, production-grade AI systems.##
** YOUR ROLE
** We're looking for Forward Deployed Engineers (FDEs), senior technologists who combine deep hands-on engineering with the judgement and presence to operate as a trusted advisor at client sites. This isn't a traditional consulting or traditional engineering role. It's both, 'll be the person a client CTO calls when something needs to be architected, built, and defended in the same  this role, you will:
* Embed directly within client environments (often on-site or hybrid) to design and build bespoke AI and agentic solutions against live, high-stakes business problems.
* Own technical architecture end to end: from initial discovery and stakeholder workshops through to production deployment, taking personal accountability for delivery quality.
* Move fluidly between hands-on coding (backend, frontend, infrastructure) and senior stakeholder engagement, including presenting architecture, trade-offs, and roadmap directly to Director and C-suite audiences.
* Rapidly prototype solutions in ambiguous, fast-moving environments, then harden them into robust, scalable, secure production systems.
* Build and run evaluation frameworks that give clients confidence in model and system behaviour, treating evals as a first-class part of the delivery lifecycle rather than an afterthought.
* Act as a technical anchor for delivery teams, setting engineering standards, unblocking hard problems, and mentoring engineers around evaluation-driven development and agentic best practice.
* Shape commercial conversations: sizing effort, identifying scope risk, and contributing to how engagements are structured and priced.
* Build and protect trust with client technical leadership, acting as the credible link between what's technically possible and what the business actually needs.
** Additional Opportunities
**** Business Development
** Lead technical work streams on proposals, RFPs, and client pitches, and act as a technical authority in sales conversations.

Shape new propositions around agentic AI and forward-deployed delivery models.
** Internal Initiatives
** Define engineering standards and reusable patterns for agentic delivery across accounts.

Contribute to practice-building, recruitment, and capability development for the wider AI engineering community.
** Learning and Development
** Access training and certifications aligned to emerging cloud, AI, and architecture domains.

Contribute to thought leadership on forward-deployed engineering as a delivery model.##
** YOUR PROFILE
**** Seniority and Experience
*** Significant experience as a senior or principal-level software engineer or architect, with a track record of owning systems end to end in production.
* Demonstrated experience operating client-facing or customer-facing in a consulting, professional services, or startup environment, comfortable being judged on both code and client outcomes.
* Experience working directly with senior stakeholders (CTO, VP Engineering, Director level), including running workshops and presenting technical strategy to non-technical audiences.
** Technical Expertise
*** Backend Development:
Deep experience building production microservices, ideally Python or Type Script.
* Frontend Development:
Strong experience with React and Type Script, using frameworks such as Next.js or Vite.
* AI and Agentic Systems:
Hands-on experience designing and deploying agentic architectures using frameworks such as Lang Chain, Lang Graph, CrewAI, or the Anthropic Agent SDK, including tool use, MCP servers, and multi-agent orchestration.
* LLM

Experience:

Practical, production experience with LLM providers (Anthropic, OpenAI, Mistral, Llama) and an informed view on model selection trade-offs.
* Cloud Platforms:
Strong proficiency in AWS or Azure or GCP, including designing for scale, security, and cost.
* Containerisation and Orchestration:
Hands-on experience with Docker, Kubernetes, and Docker Compose.
* Infrastructure as Code:
Proficiency with Terraform or equivalent.
* Evaluation and Monitoring:

Experience with evaluation-driven development and LLM observability tooling such as…