Strategic Adoption AI Engineer job opportunity at Barclays.

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Barclays Strategic Adoption AI Engineer
Experience: 1-year
Pattern: hybrid
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Engineering

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degreeBachelor's (B.Sc.)
loacation Pune, India
loacation Pune....India

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. Strong programming skills in Python and experience with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch) Experience with Jenkins, GitHub Actions, or GitLab CI/CD for automating ML pipelines Strong knowledge of Docker and Kubernetes for scalable deployments Deep experience with AWS services (e.g., SageMaker, Bedrock, Lambda, CloudFormation, Step Functions, S3 and IAM). __ Managing infrastructure for training and inference using AWS S3, EC2, EKS, and Step Functions. Experience with Infrastructure as Code (e.g., Terraform, AWS CDK) Familiarity with model lifecycle management tools (e.g., MLflow, SageMaker Model Registry). Strong understanding of DevOps principles applied to ML workflows. Hands-on experience in the development and deployment of Agentic AI solutions, leveraging Infrastructure-as-Code (IaC) models and API development using CrewAI is highly desirable. __ Exposure to product and technology evergreening, including the design and implementation of intelligent agents, crew-based architectures, and reusable modernization patterns for platform transformation. Some other highly valued skills may include below: Experience with Snowflake, Databricks for collaborative ML development and scalable data processing. Knowledge of data engineering tools (e.g., Apache Airflow, Kafka, Spark). Understanding model interpretability, responsible AI, and governance. Contributions to open-source MLOps tools or communities. __ Strong leadership, communication, and cross-functional collaboration skills. Knowledge of data privacy, model governance, and regulatory compliance in AI systems. Exposure to LangChain, Vector DBs (e.g., FAISS, Pinecone), and retrieval-augmented generation (RAG) pipelines. Exposure to practical application of Prompt Engineering techniques, including #designing, #optimizing, and #evaluating prompts for AI-driven workflows and intelligent agent behavior.

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