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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Platform Engineer- Senior Consultant-AI and Digital Factory - **Company:** Capgemini Invent - **Location:** Bristol, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Bash Shell, Cloud Engineering, Databases, Continuous Integration, DevOps, Failover, Github, Python (Programming Language), OpenShift, Performance Tuning, Ansible, Azure Machine Learning, AI Infrastructure, Large Language Models, Multi-Agent Systems, Multi-Cloud, Gitlab, Git, Containerization, AI Platforms, Tanzu, Git Flow, Kubernetes, Bicep, Machine Learning Operations, Terraform, Software Version Control, Serverless Computing, Databricks, Vmware - **Published:** September 28, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=5b4531a14f287063 ## About the Role You'll bring deep, hands-on experience in most of the areas below, with strong depth in AI/GenAI platform engineering specifically. You don't need to tick every box. AI & GenAI Platform Engineering * Model serving and gateway infrastructure (e.g. vLLM, LiteLLM, managed endpoints), with routing, failover, and per-workload cost attribution * Agent orchestration and tool-calling frameworks (e.g. LangGraph or equivalent), including familiarity with the Model Context Protocol (MCP) * Evaluation engineering (golden datasets, regression gates in CI, LLM-judge calibration) * Guardrail and AI-observability tooling (e.g. NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, Braintrust) MLOps & LLMOps * Hands-on with MLOps platforms (Azure ML, Databricks, SageMaker) and vector/retrieval databases (Pinecone, Milvus, pgvector) * Experience with GPU-accelerated infrastructure and NVIDIA AI Enterprise or equivalent stacks * Exposure to fine-tuning, RLHF, or SLM distillation is a strong plus Cloud-Native & Infrastructure * Deep expertise in Kubernetes and container platforms (OpenShift, AKS, EKS, GKE, or VMware Tanzu) * Infrastructure as Code and DevOps practices (Terraform, Bicep, Ansible, GitOps and CI/CD pipelines) * 5+ years' experience across Azure, AWS, or GCP; strong DevOps fundamentals Consulting & Delivery * Proven experience in DevOps processes and best practices, applied in a client-facing consulting context * Ability to lead architecture reviews, workshops, and executive briefings * Strong soft skills key to consulting: time and risk management, problem solving, leading with empathy * Ability to converse with both internal and external stakeholders, always holding the appropriate level of conversation General * Strong scripting ability (Python plus one systems language such as Go or Rust, or Bash) * Experience with financial services, insurance, or other regulated-industry engineering is a strong plus, given the compliance demands of AI in these sectors * Experience using version control software (Git, GitHub, GitLab) ## Description Our AI Platform Engineering Senior Consultants sit within AI & Digital Factory, part of the Business Technology practice in Capgemini Invent, which leads digital transformation projects across Capgemini. We are a dynamic community that values innovation, professional development, and the ability to make a real difference for our clients. As an AI Platform Engineer, you'll design, build, and operate the infrastructure that enterprise AI and Generative AI workloads run on: the platform layer beneath LLMs, agents, and MLOps pipelines. This spans GPU-accelerated compute and container platforms, model serving and gateway infrastructure, evaluation and guardrail systems, and the MLOps/LLMOps tooling that takes a model from experiment to production. You'll work across hybrid and multi-cloud environments, helping clients modernize their AI infrastructure and adopt AI safely and at scale. As part of your role, you will: * Be a senior or lead engineer on client AI platform engagements * Architect and deploy AI-ready infrastructure (GPU-accelerated compute, Kubernetes/OpenShift, and cloud-native services) across cloud, on-premises, and hybrid environments * Build and operate core AI platform components: model serving and gateway infrastructure, agent orchestration and tool-calling frameworks, evaluation harnesses, and guardrail/governance layers * Implement MLOps and LLMOps pipelines (model deployment, monitoring, retraining, and fine-tuning where relevant) using Infrastructure-as-Code, GitOps, and CI/CD * Establish observability, security, and governance frameworks specific to AI systems, including cost attribution and lifecycle management * Work with clients and internal teams to develop new opportunities and shape a strong AI platform engineering culture * Lead client workshops, architecture reviews, and technical briefings; provide operational support including monitoring and troubleshooting * Share your knowledge and experience with colleagues as you coach and mentor them, while developing your own skills by experimenting with and learning new technologies As part of your role you will also have the opportunity to contribute to the business and your own personal growth, through activities that form part of the following categories: Leadership People/line manager, stakeholder management and communication, recruitment, etc. Help to shape the AI Platform Engineering offering. Business Development Leading/contributing to proposals, RFPs, bids, proposition development, client pitch contribution, client hosting at events. Internal contribution Internal projects, campaign development, internal think-tanks, whitepapers, practice development (operations, recruitment, team events & activities), offering development. Learning & development Training to support your career development and the skills demand within the company, certifications etc. ## Related Videos - [The Private AI Platform: Why Agentic Apps Need a Private Application Platform](https://www.wearedevelopers.com/videos/100162-the-private-ai-platform-why-agentic-apps-need-a-private-application-platform) - [Back(end) to the Future: Embracing the continuous Evolution of Infrastructure and Code](https://www.wearedevelopers.com/videos/440-back-end-to-the-future-embracing-the-continuous-evolution-of-infrastructure-and-code) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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