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
Analyst Expectations
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.
Join Barclays as an Applied - AI Engineer role, where you will build and deliver AI/ML features for our enterprise AI capabilities. Working within an experienced engineering team, you will develop agentic AI solutions, implement LLM-powered applications, and contribute to building secure, scalable AI systems that serve thousands of colleagues across the organisation. This is a hands-on engineering role with significant growth opportunity in GenAI, agentic AI, and applied machine learning. At Barclays, we don't just anticipate the future - we're creating it.
To be successful in this role, you should have below skills:
Bachelor's degree (or above) in Computer Science, Engineering, Mathematics, or related discipline
3+ years software engineering experience developing applications at scale on cloud platforms (AWS, Azure, or GCP)
Proficiency in Python (preferred) or Java/Go — with experience building REST APIs, working with async frameworks, and writing clean, testable code
Experience with GenAI/LLM systems — calling LLM APIs, building prompts, implementing RAG patterns, or working with embedding models
Working experience with at least one agentic AI framework (Strands, LangGraph, or Google ADK) and familiarity with MCP and A2A protocols
Experience with AWS services — EC2/EKS, S3, IAM, RDS, and at least one AI service (Bedrock, SageMaker)
Hands-on experience with Docker and Kubernetes — containerising applications and deploying to orchestrated environments
Experience with CI/CD pipelines and version control (Git/GitLab) — understanding of automated build, test, and deployment workflows
Solid understanding of REST API design, JSON/gRPC, and integration patterns
Good problem-solving skills with ability to work independently and deliver to specifications; clear written and verbal communication
Some other highly valued skills may include below:
Experience with vector databases, semantic search, or embedding-based retrieval systems
Experience with observability tools — OpenTelemetry, structured logging, or distributed tracing
Understanding of AI safety concepts — prompt injection risks, PII handling, content filtering, toxicity detection
Experience implementing evaluation pipelines for LLM or agentic systems (benchmarking, regression detection)
Knowledge of AI security frameworks — NIST AI RMF, MITRE ATLAS, OWASP LLM Top 10
Experience with Infrastructure-as-Code (CloudFormation, Terraform) and GitOps workflows
Familiarity with model fine-tuning or training techniques (supervised learning, LoRA)
Understanding of context management and memory systems for stateful AI agents
Knowledge of banking, financial products, and services
Understanding of OAuth2/OIDC identity flows and JWT token handling
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 is based in Bengaluru and Pune office.