About this role
Barclays is hiring an AI Engineer in Noida to design, develop, deploy and support enterprise-scale AI and Generative AI solutions for customer servicing, operations, productivity and software engineering. The role takes hands-on ownership of AI application architecture, cloud infrastructure, deployment pipelines and production operations, working with Product Owners, Data Scientists, Data Engineers and MLOps teams. It is a permanent, full-time position. Barclays describes a hybrid working approach with fixed onsite anchor days; the role’s working pattern should be discussed with the hiring manager.
Responsibilities
- Build production AI applications using retrieval-augmented generation, conversational AI, agentic workflows and intelligent automation. Develop scalable backend services, APIs and integrations with enterprise data platforms and business applications.
- Architect containerized, cloud-native workloads, including Kubernetes and Amazon EKS. Maintain CI/CD pipelines, infrastructure as code, model deployment frameworks, monitoring and operational telemetry. Design secure networking and optimize performance, availability, scalability and cost.
- Apply MLOps and LLMOps practices covering model versioning, automated testing, deployment, observability, rollback and lifecycle management. Meet governance, security, resiliency, responsible AI and audit requirements.
- The posting also lists data analytics accountabilities: collect and prepare data, maintain acquisition and processing pipelines, develop statistical, machine-learning and predictive models, and work with stakeholders to identify opportunities to use data.
Required qualifications
- A bachelor’s degree in Computer Science, Engineering, Mathematics or a related discipline; relevant software engineering experience, including building and deploying AI/ML or Generative AI applications in production. No minimum number of years is stated.
- Strong Python skills and experience with AI orchestration frameworks such as LangChain, LangGraph, CrewAI or AutoGen; hands-on experience with RAG, vector databases, semantic search and LLM integrations.
- Experience operating applications on AWS, including EKS, ECS, Lambda, Bedrock, SageMaker and S3; knowledge of Docker, Kubernetes, Helm and container security. Networking knowledge must cover TCP/IP, DNS, VPCs, IAM, gateways, load balancers and private connectivity. Experience with production CI/CD tooling and sound software engineering, testing, security and operational-support practices is required.
Preferred experience includes multi-agent systems, regulated AI platforms, Kubernetes administration, infrastructure automation with Terraform or CloudFormation, and large-scale AI deployment programs. At Assistant Vice President level, the posting expects cross-functional influence, risk and control ownership, and either people leadership or leadership of collaborative assignments.