AI Engineer
You'll use your expertise to design, build, and scale AI/ML solutions — from agentic AI systems and LLM applications to end-to-end MLOps pipelines — enabling the bank's success across multiple business units.
Date live: 07/30/2026
Business Area: Chief Technology Office
Area of Expertise: Technology
Contract: Permanent
Reference Code: JR-0000101368
Join Barclays as a Vice President – AI Engineer role, where you will serve as a senior engineer helping to drive adoption of state-of-the-art AI technologies. You'll use your expertise to design, build, and scale AI/ML solutions — from agentic AI systems and LLM applications to end-to-end MLOps pipelines — enabling the bank's success across multiple business units. You will shape architectural decisions, mentor engineering teams, and ensure our AI solutions meet the demands of enterprise-grade reliability, security, and governance. At Barclays, we don't just anticipate the future - we're creating it.
To be successful in this role, you should have below skills:
Bachelor's degree (or above) in Computer Science, Engineering, Mathematics, or related discipline
12+ years software engineering experience or experience designing and building scalable ML infrastructure, model serving platforms, and end-to-end MLOps systems at enterprise scale
Expert-level proficiency in Python (FastAPI, async patterns) and/or Go/Java, with demonstrable experience building production systems serving high-throughput, low-latency workloads
Strong experience developing agentic AI systems and LLM applications including multi-agent orchestration, tool use, planning frameworks, and production deployment of multi-layered AI workflows at scale
Deep technical knowledge of LLM fine-tuning, prompt engineering, RAG architectures, and modern agentic frameworks (Strands, LangGraph, Google ADK) with validated ability to architect and optimise agent-based solutions
Hands-on experience with GenAI/LLM systems — prompt engineering, loop engineering, retrieval-augmented generation, model serving (vLLM), and OpenAI-compatible API surfaces
Experience with AWS services — EC2/EKS, S3, IAM, RDS, and at least one AI service (Bedrock, SageMaker)
Hands-on experience with Docker and Kubernetes — containerising applications and deploying to orchestrated environments
Experience with distributed systems architecture — microservices, cloud platforms, active-active replication, failover strategies, and data consistency patterns
Proven ability to communicate complex technical architectures to senior leadership and cross-functional stakeholders
Some other highly valued skills may include below:
Experience building evaluation frameworks for agentic systems including agent benchmarking, reasoning traces, and monitoring observability for multi-step AI workflows in production
Experience in vector databases, knowledge graphs, semantic search, and memory systems for stateful agents with understanding of retrieval optimisation and context management
Posted July 30, 2026