Capgemini
Lead AI Engineer
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Lead AI Engineer – Job Description
About the Role
We’re seeking a Lead AI Engineer who can design, build, and operationalise advanced AI, Machine Learning, and Generative AI systems at enterprise scale.
The focus of this role is to bridge the gap between AI prototypes and embedding data and AI solutions in business. You’ll scale AI solutions responsibly and reliably, ensuring they move from lab to live by building the right solutions, practices, and guardrails while ensuring business value creation and impact.
In this position, you will play a key part in:
- Designing and delivering end-to-end AI/ML systems, from data preparation and model development to model deployment, feature stores, model management, and monitoring
- Delivering solutions using the latest GenAI and agentic frameworks, such as ADK, LangGraph, Microsoft Agent Framework, LlamaIndex, and others
- Translating AI use case requirements into data and AI architectures using the most suitable cloud services across hyperscalars
- Leading multi-disciplinary teams to execute complex requirements
- Architecting and implementing Generative AI solutions, including RAG pipelines, agentic workflows, and orchestration of large language models across Azure, GCP, or AWS
- Embedding safety, evaluation, and assurance mechanisms across the AI lifecycle, ensuring solutions are ethical, explainable, and responsible
- Collaborating with Product Managers, Data Scientists, and Business stakeholders to ensure AI solutions drive business value and impact
As part of your role, you will also have the opportunity to contribute to business and personal growth, focusing on:
- Business Development – Build client-ready demos/POCs, support proposals and technical deep-dives, and showcase delivery patterns
- Internal contribution – Build reusable assets and frameworks to accelerate delivery across accounts and support capability development by contributing to internal communities and best practices
- Capability Development – Contribute to thought leadership, blog posts, or internal accelerator development in emerging AI engineering topics, including Agentic AI, LLMOps, or evaluation frameworks
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What You Will Bring
We’d love to meet someone with:
- Experience working in a major Consulting firm, and/or in industry but with a Consulting mindset, demonstrating a proven ability to succeed in a matrixed organisation. Capability to enlist support and commitment from peers in selling and delivering solutions. Experience of working with client sponsors (technical and non-technical) to collaboratively design requirements and build solutions
- Experience of designing and implementing MLOps strategy and framework, along with a proven track record in designing and delivering AI/ML solutions at scale, from concept to production
- A deep understanding of Generative AI and Agentic AI – including RAG pipelines, embeddings, evaluation harnesses, and orchestration frameworks
- Experience designing cloud-native data and AI architectures across Azure, GCP, AWS, and/or Databricks
- The ability to demonstrate the potential that scaling AI unlocks business value and impact
Experience Required
Key Technical Expertise
(A mix of these skills is expected—having a strong foundation across the scope and adaptability to new tools is crucial.)
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Experience with deploying and scaling AI solutions using at least one major cloud platform:
- Azure: Foundry, AI Studio, OpenAI, AKS
- GCP: Vertex AI, Cloud Run
- AWS: Bedrock, SageMaker
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Experience building and automating AI/ML pipelines using tools such as MLflow, Kubeflow, Azure ML, Vertex Pipelines, Airflow, or Google ADK
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Hands-on experience with Generative and Agentic AI frameworks, including:
- LangChain, LlamaIndex, CrewAI, Autogen, Google ADK
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Ability to design and implement RAG pipelines, agentic workflows, and integration with LLM APIs (e.g., OpenAI, Anthropic, Hugging Face)
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- Proficiency in CI/CD and containerisation, including:
- GitHub Actions, Azure DevOps, Docker, Kubernetes
Nice to Haves
- Familiarity with evaluating AI system performance (e.g., prompt evaluation, A/B testing, and quality assessment frameworks)
- Understanding of modern data patterns, such as:
- Lakehouse architectures
- Vector databases
- Relational/NoSQL stores
- Knowledge of API gateways, event streaming, and general integration patterns
Eligibility
- You must have resided in the UK for the last 5 years to apply for this role.
About Capgemini
What You’ll Love About Working Here
- Join a dynamic, ambitious team that brings the latest tech to clients in production, making a meaningful real-world difference
- Balance innovation, deployment, and tangible impact across public and private sectors
- A fast-moving, tech-driven culture where curiosity and experimentation are encouraged
- Opportunities to contribute to cutting-edge internal projects, highlighting emerging models and agentic frameworks
- Collaborate with other real innovators passionate about shaping the future with technology
Need to Know
Work Culture & Benefits
- Ranked Glassdoor Best Places to Work UK for 2 consecutive years
- Actively committed to Diversity & Inclusion
- Supports flexible working, including hybrid arrangements
- Invests in employee wellbeing, including trained mental health champions and apps like Thrive and Peppy
- Focused on sustainability, aiming to reduce carbon footprint and improve digital access
- Named one of the world’s most ethical companies by Ethisphere Institute
Logistics
- Work is assigned across different locations; temporary periods away at short notice are possible.
Compensation & Benefits
- Offers a remuneration package with flexible benefits and a variable element based on grade, company performance, and individual contributions
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