📍

AI Platform Engineer

No experience
Finance
Software engineering
Posted:
December 28, 2025

Barclays

Personal and corporate banking
76.1
Palpable Score
Apply >view company >

Join Us as an AI Platform Engineer – Shape the Future of AI at Barclays.


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.



To be successful as an AI Platform Engineer at this level, you should have experience with:


•    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.



Some other highly valued skills may include:

•    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

You may be assessed on the key critical skills relevant for success in role, such as risk and controls, change and transformation, business acumen strategic thinking and digital and technology, as well as job-specific technical skills.

This role can be based out of our Glasgow or Canary Wharf office.

Purpose of the role

To design, develop and improve software, utilising various engineering methodologies, that provides business, platform, and technology capabilities for our customers and colleagues.

Accountabilities

Vice President Expectations

About the company

Barclays

Company overview
Barclays is a multinational bank and financial services group with major businesses in UK retail banking and international corporate and investment banking. Barclays serves consumers, small businesses, corporates, and institutional clients through products like lending, payments, cards, markets, and wealth services. Barclays also runs large technology, operations, and data teams that build and maintain banking platforms at scale. Barclays is headquartered in London and operates globally.

Locations and presence

Barclays has major UK hubs including London (Canary Wharf), Glasgow, Manchester, Knutsford (Radbroke), and Northampton, alongside a wide international footprint. Barclays has tightened office attendance expectations in recent years, with public reporting describing a minimum three-days-a-week in-office baseline for many roles, while client-facing teams can be more office-heavy.

Palpable Score

76.1
/ 100
Barclays gives graduates and students multiple reliable entry points through internships, a structured graduate programme (Explorer and Expert paths), apprenticeships, and an earlier Discovery programme. The main downside is that, despite clear official stage descriptions, candidate communication and closure can still feel uneven at scale. Learning support and early-career community design are strong, while published outcome metrics like conversion rates and time-to-promotion are still limited.
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