capco
Senior AI Engineer
At a Glance
- Location
- UK - London
- Posted
- 2026-07-21T12:12:19-04:00
Key Requirements
Required Skills
Domain Knowledge
- Banking
- Engineering
- Finance
Benefits & Perks
Flexibility : 5 weeks of annual leave with the option to buy or sel
ompetitive pension, health insurance, life insurance and critical illness co
Requirements
Proven hands-on experience deploying LLMs and multi-modal AI models within enterprise or large-scale production environments
Strong software engineering expertise in Python, including backend development, API design, and distributed systems
Solid understanding of scalable MLOps, observability, CI/CD practices, and cloud-native AI deployment architectures
Experience building AI agent frameworks or autonomous orchestration platforms
Knowledge of vector databases, semantic search, and advanced RAG architectures
Experience with cloud platforms such as AWS, Azure, or GCP for AI workloads
Compensation & Benefits
We offer a competitive, people-first benefits package designed to support every aspect of your life:
Core Benefits:
Discretionary bonus, competitive pension, health insurance, life insurance and critical illness cover.
Mental Health:
Easy access to CareFirst, Unmind, Aviva consultations, and in-house first aiders.
Family-Friendly:
Responsibilities
We’re looking for a Senior AI Engineer (Senior/Principal Consultant) to join our Technology Delivery team.
In this role, you’ll combine deep expertise in AI/ML engineering and software development to design, build, and deploy advanced generative AI and agentic systems for leading financial services clients. You’ll work hands-on across the full AI engineering lifecycle, from architecting intelligent multi-agent systems to deploying scalable production-grade AI applications within enterprise environments.
As a senior consultant, you’ll also collaborate closely with multidisciplinary teams and client stakeholders to drive innovation, accelerate adoption, and deliver measurable business impact.
Design and implement agentic workflows using prompt engineering, Retrieval-Augmented Generation (RAG), APIs, and enterprise data integrations
Build scalable MLOps pipelines and cloud-native AI applications to support secure, production-grade deployments
Collaborate with clients and cross-functional teams to shape AI engineering strategy and accelerate GenAI adoption across complex environments