AI Architecture Lead
- Company
- Barclays
- Location
- London
- Job type
- Full Time
- Salary
- Negotiable
- Deadline
- 2026-12-18
Job description
To provide quantitative and analytical expertise to support trading strategies, risk management, and decision-making within the investment banking domain, applying quantitative analysis, mathematical modelling, and technology to optimise trading and investment opportunities.
Responsibilities
- Experience deploying and operating machine learning models within enterprise environments.
- Experience with LLM-powered applications, agent frameworks and retrieval systems.
- Familiarity with prompt engineering, evaluation frameworks and AI safety controls.
- Experience with reinforcement learning, fine-tuning or model customisation techniques.
- Experience with Kubernetes and containerised deployment models.
- Experience managing or designing GPU-enabled environments.
- Experience with distributed training and inference workloads.
- Understanding of vector databases, model serving frameworks and AI infrastructure tooling.
- Experience with cloud platforms such as Azure, AWS or GCP.
- Prior experience in electronic trading, quantitative development or financial markets.
- Experience integrating AI capabilities into business workflows and production systems.
- Familiarity with modern C++ and Python-based quantitative environments.
- Design and develop shared AI platform capabilities used across Global Markets.
- Build scalable infrastructure for machine learning and generative AI workloads.
- Define architectures for model deployment, inference, orchestration and monitoring.
Requirements
- Proven track record delivering large-scale engineering or AI platform initiatives.
- Expert Python development skills.
- Strong software engineering and architecture experience.
- Experience building production machine learning or generative AI systems.
- Excellent understanding of distributed systems, APIs and modern software architectures.
- Experience working with cloud-native environments and container platforms.
- Experience with CI/CD, automated testing, observability and production support.