Lead AI Engineer – London
Reference Code: 404560-en_GB
Contract Type: Permanent
Professional Communities: Data & AI
About the Role
Lead AI Engineer
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 model management
- Delivering solutions using the latest GenAI and Agentic Frameworks, such as ADK, Langgraph, Microsoft Agent Framework, Llamaindex and other
- 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 drives business value and impact.
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:
- Business Development – Build client-ready demos/POVs, support proposals and technical deep-dives, and showcase delivery patterns.
- Internal contribution – Build reusable assets and frameworks that accelerate delivery across accounts and support capability development by contributing to our internal communities and best practices.
- Capability Development – Contribute to thought leadership, blog posts, or internal accelerator development in emerging AI engineering topics such as Agentic AI, LLMOps, or evaluation frameworks.
What you will bring
We’d love to meet someone with:
- Experience working in a major Consulting firm, and/or in industry but having a Consulting mindset with a proven ability to be successful in a matrixed organisation, and to enlist support and commitment from peers in selling and delivering solutions. Experience of working with client sponsors, both technical and non-technical, to collaboratively design requirements and build out solutions.
- Experience of designing and implementing MLOPs strategy and framework and proven track record in designing and delivering AI/ML solutions at scale, from concept to production.
- Deep understanding of Generative AI and Agentic AI — 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
Your Technical Expertise:
This list shows the technologies we work with most often. We don’t expect you to have experience in all of them – what matters is a strong foundation and a good cross-section of these skills, along with the adaptability and curiosity to learn new tools as projects demand. We like to innovate and need self-driven, fast-paced learners in our team.
- 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)
- Experience building and automating AI/ML pipelines using tools such as MLflow, Kubeflow, Azure ML, Vertex Pipelines, Airflow or Google ADK
- Hands‑on experience with Generative and Agentic AI frameworks such as LangChain, LlamaIndex, CrewAI, Autogen, Google ADK, or similar.
- Ability to design and implement RAG pipelines, agentic workflows, MCP and integration with LLM APIs (OpenAI, Anthropic, Hugging Face OR similar).
- Proficiency in CI/CD and containerisation: GitHub Actions, Azure DevOps, Docker, Kubernetes.
- Nice to haves: Familiarity with evaluating AI system performance, including prompt evaluation, A/B testing, and quality assessment frameworks.
- Nice to haves: Understanding of modern data patterns: lakehouse architectures, vector databases, and relational/NoSQL stores
- Nice to haves: Familiarity with API gateways, event streaming and general integration patterns.
- Eligibility: You need to have resided in the UK for the last 5 years to be able to apply for this role
Lead AI Engineer – London
Reference Code: 404560-en_GB
Contract Type: Permanent
Professional Communities: Data & AI
About the Role
Lead AI Engineer
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 model management
- Delivering solutions using the latest GenAI and Agentic Frameworks, such as ADK, Langgraph, Microsoft Agent Framework, Llamaindex and other
- 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 drives business value and impact.
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:
- Business Development – Build client-ready demos/POVs, support proposals and technical deep-dives, and showcase delivery patterns.
- Internal contribution – Build reusable assets and frameworks that accelerate delivery across accounts and support capability development by contributing to our internal communities and best practices.
- Capability Development – Contribute to thought leadership, blog posts, or internal accelerator development in emerging AI engineering topics such as Agentic AI, LLMOps, or evaluation frameworks.
What you will bring
We’d love to meet someone with:
- Experience working in a major Consulting firm, and/or in industry but having a Consulting mindset with a proven ability to be successful in a matrixed organisation, and to enlist support and commitment from peers in selling and delivering solutions. Experience of working with client sponsors, both technical and non-technical, to collaboratively design requirements and build out solutions.
- Experience of designing and implementing MLOPs strategy and framework and proven track record in designing and delivering AI/ML solutions at scale, from concept to production.
- Deep understanding of Generative AI and Agentic AI — 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
Your Technical Expertise:
This list shows the technologies we work with most often. We don’t expect you to have experience in all of them – what matters is a strong foundation and a good cross-section of these skills, along with the adaptability and curiosity to learn new tools as projects demand. We like to innovate and need self-driven, fast-paced learners in our team.
- 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)
- Experience building and automating AI/ML pipelines using tools such as MLflow, Kubeflow, Azure ML, Vertex Pipelines, Airflow or Google ADK
- Hands‑on experience with Generative and Agentic AI frameworks such as LangChain, LlamaIndex, CrewAI, Autogen, Google ADK, or similar.
- Ability to design and implement RAG pipelines, agentic workflows, MCP and integration with LLM APIs (OpenAI, Anthropic, Hugging Face OR similar).
- Proficiency in CI/CD and containerisation: GitHub Actions, Azure DevOps, Docker, Kubernetes.
- Nice to haves: Familiarity with evaluating AI system performance, including prompt evaluation, A/B testing, and quality assessment frameworks.
- Nice to haves: Understanding of modern data patterns: lakehouse architectures, vector databases, and relational/NoSQL stores
- Nice to haves: Familiarity with API gateways, event streaming and general integration patterns.
- Eligibility: You need to have resided in the UK for the last 5 years to be able to apply for this role
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