Vista previa de la oferta

AI Architecture Lead

lead · Investment Banking

Job Description

Purpose of the role

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. 

Accountabilities

• AI Engineering & Platform Development

• 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.
• Develop reusable frameworks, APIs and services that accelerate AI delivery across multiple teams.
• Evaluate emerging technologies and establish best practices for the adoption of AI at scale.

• MLOps & LLMOps

• Design and implement MLOps and LLMOps frameworks supporting the full model lifecycle.
• Establish standards for model deployment, versioning, evaluation, monitoring and observability.
• Improve reproducibility, governance and operational resilience of AI systems.
• Define processes and tooling for prompt management, agent evaluation and model performance tracking.
• Build automation that improves the speed and quality of AI development and deployment.

• Cloud & Infrastructure

• Design cloud-native architectures supporting AI applications and services.
• Optimise utilisation of GPU infrastructure and specialised compute resources.
• Partner with Technology teams on containerisation, orchestration and deployment strategies.
• Improve scalability, reliability and cost efficiency of AI workloads.
• Contribute to the evolution of the firm's AI platform and infrastructure strategy.

•  

  Essential Experience

• 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.

Director Expectations

• To manage a business function, providing significant input to function wide strategic initiatives. Contribute to and influence policy and procedures for the function and plan, manage and consult on multiple complex and critical strategic projects, which may be business wide..
• They manage the direction of a large team or sub-function, leading other people managers and embedding a performance culture aligned to the values of the business. Or for an individual contributor, they lead organisation wide projects and act as deep technical expert and thought leader, identifying new ways of working and collaborating cross functionally. They will train, guide and coach less experienced specialists and provide information affecting long term profits, organisational risks and strategic decisions..
• Provide expert advice to senior functional management and committees to influence decisions made outside of own function, offering significant input to function wide strategic initiatives.
• Manage, coordinate and enable resourcing, budgeting and policy creation for a significant sub-function.
• Escalates breaches of policies / procedure appropriately.
• Foster and guide compliance, ensure regulations are observed that relevant processes in place to facilitate adherence.
• Focus on the external environment, regulators, or advocacy groups to both monitor and influence on behalf of Barclays, when appropriate.
• Demonstrate extensive knowledge of how the function integrates with the business division / Group to achieve the overall business objectives.
• Maintain broad and comprehensive knowledge of industry theories and practices within own discipline alongside up-to-date relevant sector / functional knowledge, and insight into external market developments / initiatives.
• Use interpretative thinking and advanced analytical skills to solve problems and design solutions in often complex/ sensitive situations.
• Exercise management authority to make significant decisions and certain strategic decisions or recommendations within own area.
• Negotiate with and influence stakeholders at a senior level both internally and externally.
• Act as principal contact point for key clients and counterparts in other functions/ businesses divisions.
• Mandated as a spokesperson for the function and business division.

All Senior Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others.

All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.

AI & Machine Learning

• 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.

Platform & Infrastructure

• 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.

Markets & Quantitative Development

• 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.