Barclays ML Engineer – Gen AI and Machine Learning Opportunity | Barclays | 2025 | Experience

About Barclays

Barclays is a British multinational universal bank headquartered in London, England. With a history dating back over 300 years, Barclays operates in more than 40 countries and employs over 83,000 people worldwide. The bank provides retail banking, credit cards, corporate and investment banking, and wealth management services to customers and clients globally. Barclays is known for its strong commitment to innovation and technology, making it one of the leading financial institutions in the digital transformation space. The company has consistently invested in cutting-edge technologies including artificial intelligence, machine learning, blockchain, and cloud computing to enhance customer experiences and drive operational efficiency.

Job Description

The Barclays ML Engineer role represents an exceptional opportunity for experienced professionals to work at the intersection of finance and advanced artificial intelligence technologies. This position focuses on developing and deploying next-generation AI systems that will transform how Barclays serves its customers and manages its operations. The role requires a unique combination of technical expertise in machine learning, cloud computing, and enterprise-scale system architecture. As a Barclays ML Engineer, you will be part of a dynamic team that is shaping the future of financial technology through intelligent automation, generative AI applications, and conversational AI solutions. This is not just a development role but a leadership position where you will own the entire AI product lifecycle from conceptualization to production deployment.

Job Overview

Roles & Responsibilities

• Design & Deploy Gen AI Systems: Build and implement Generative AI applications using AWS Bedrock and SageMaker platforms. Develop LLM-based use cases including Retrieval-Augmented Generation (RAG), text summarization, data analysis, and content generation systems that can scale to enterprise requirements.
• LLM Optimization & Prompt Engineering: Fine-tune Large Language Models for specific financial use cases, design and optimize prompts for maximum performance, ensure AI safety and ethical considerations, and implement evaluation frameworks to measure model effectiveness and accuracy.
• Cloud & Architecture Development: Develop scalable AI pipelines on AWS infrastructure, work with vector databases including FAISS, PGVector, and DynamoDB for efficient similarity search and retrieval operations, integrate APIs and cloud-native architectures, and ensure high availability and fault tolerance of AI systems.
• Collaboration & Leadership: Bridge business and technical teams by translating business requirements into technical specifications, mentor junior developers and data scientists, lead technical discussions with stakeholders, and drive the adoption of AI solutions across the organization.
• End-to-End AI Product Lifecycle: Own the complete lifecycle from problem identification and solution design to implementation, testing, deployment, and monitoring. Ensure proper documentation, maintain code quality, and establish best practices for AI development and deployment.
• Performance Monitoring & Optimization: Implement monitoring systems to track model performance, identify and resolve performance bottlenecks, optimize inference times and resource utilization, and ensure compliance with regulatory requirements for financial AI systems.

Eligibility Criteria & Skills

• Programming Expertise: Strong proficiency in Python programming with experience in developing production-grade applications. Understanding of software engineering principles, design patterns, and best practices for writing clean, maintainable, and efficient code.
• AI Frameworks & Libraries: Hands-on experience with LangChain, LangGraph, CrewAI, and Langfuse for building complex AI applications. Knowledge of TensorFlow, PyTorch, and other deep learning frameworks for model development and training.
• Cloud Computing: Extensive experience with AWS services including Bedrock for foundation models, SageMaker for machine learning, Lambda for serverless computing, and other related services. Understanding of cloud architecture patterns and best practices.
• Database Technologies: Proficiency in SQL and NoSQL databases, experience with vector databases for AI applications, understanding of data modeling and optimization techniques for large-scale data processing.
• Machine Learning Concepts: Strong foundation in machine learning algorithms, deep learning architectures, natural language processing, and computer vision. Experience with model selection, training, evaluation, and deployment in production environments.
• DevOps & CI/CD: Experience with continuous integration and deployment pipelines, Jenkins or similar automation tools, containerization technologies like Docker and Kubernetes, and infrastructure as code practices.
• Communication & Leadership: Excellent communication skills to explain complex technical concepts to non-technical stakeholders, ability to lead cross-functional teams, and experience in project management and delivery.
• Educational Background: Bachelor’s degree in Computer Science, Information Technology, Engineering, or related field. Advanced degrees or certifications in AI/ML are highly preferred.

How to Apply for Barclays Recruitment 2025?

1. Visit the official Barclays careers page through the provided link and create an account if you don’t already have one.
2. Search for the “ML Engineer – Gen AI and Machine Learning Opportunity” position in Bengaluru, India.
3. Carefully review the job description and ensure your qualifications match the requirements.
4. Prepare your updated resume highlighting your relevant experience with AI, machine learning, and cloud technologies.
5. Complete the online application form with accurate information and upload your resume.
6. Submit any additional documents or certifications that demonstrate your expertise in AI/ML technologies.
7. Wait for the recruitment team to review your application and respond with further instructions.
8. Prepare for technical interviews that may include coding challenges, system design discussions, and AI/ML concept evaluations.

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