AI Platform Engineer Job at Barclays (London)

Job Description

We’re excited to launch a groundbreaking initiative at Barclays - building a next-generation platform that empowers front-office developers (Quants and Strats) to create high-performance, AI-driven applications. As an AI Platform Engineer, you’ll play a pivotal role in designing, building, and scaling robust platform components that enable advanced AI/ML workloads across both on-premises and cloud environments. This is a hands-on engineering role where your expertise will directly influence how we deliver secure, scalable, and innovative solutions. You’ll collaborate with diverse teams, solve complex challenges, and help shape the technical direction of a platform that will transform how AI is leveraged in financial services.

Job Responsibility

Design, develop and improve software, utilising various engineering methodologies, that provides business, platform, and technology capabilities for our customers and colleagues
Development and delivery of high-quality software solutions by using industry aligned programming languages, frameworks, and tools. Ensuring that code is scalable, maintainable, and optimized for performance
Cross-functional collaboration with product managers, designers, and other engineers to define software requirements, devise solution strategies, and ensure seamless integration and alignment with business objectives
Collaboration with peers, participate in code reviews, and promote a culture of code quality and knowledge sharing
Stay informed of industry technology trends and innovations and actively contribute to the organization’s technology communities to foster a culture of technical excellence and growth
Adherence to secure coding practices to mitigate vulnerabilities, protect sensitive data, and ensure secure software solutions
Implementation of effective unit testing practices to ensure proper code design, readability, and reliability

Requirements

Proven experience in Python engineering, with a focus on backend and infrastructure tooling
Deep knowledge of AWS services (IAM, KMS, CloudFormation, API Gateway, S3, Lambda, ECS, Glue, Step Functions, MSK, EKS, Bedrock)
Experience scaling platforms for AI/ML workloads and integrating generative AI tooling
Understanding of secure software development, cloud cost optimization, and platform observability
Ability to communicate complex technical concepts clearly to technical and non-technical audiences
Demonstrated capability to guide engineering teams and influence technical strategy

Nice to have

Experience with MLOps platforms such as Databricks or SageMaker, and familiarity with hybrid cloud strategies (Azure, on-prem Kubernetes)
Strong understanding of AI infrastructure for scalable model serving, distributed training, and GPU orchestration
Expertise in Large Language Models (LLMs) and Small Language Models (SLMs), including fine-tuning and deployment for enterprise use cases
Hands-on experience with Hugging Face libraries and tools for model training, evaluation, and deployment
Knowledge of agentic frameworks (e.g., LangChain, AutoGen) and Model Context Protocol (MCP) for building autonomous AI workflows and interoperability
Awareness of emerging trends in GenAI platforms, open-source MLOps, and cloud-native AI solutions

What we offer

Competitive holiday allowance
Life assurance
Private medical care
Pension contribution

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