Join the Corporate AI Incubator team within Corporate Banking as a Gen AI QA Engineering Lead at Barclays. This role is pivotal in ensuring the quality, reliability, and compliance of cutting-edge generative AI solutions, including LLMs and retrieval-augmented generation (RAG) pipelines. You will lead QA efforts across multiple AI PODs, collaborating with data scientists, ML engineers, Data engineers and governance teams to build robust testing frameworks and automation pipelines that validate model performance, safety, and enterprise readiness.
To be successful as a Gen AI QA Engineering Lead, you should have experience with:
Designing and implementing QA frameworks for GenAI models, including prompt validation, output consistency, hallucination detection, and bias mitigation.
Building automated test suites for GenAI pipelines using Python, PyTest, and tools like MLflow, SageMaker, or similar.
Integrating QA checks into CI/CD pipelines (e.g., Jenkins, GitHub Actions, GitLab CI/CD) for continuous validation.
Monitoring model outputs for anomalies and ensuring high-quality data ingestion and refresh cycles in collaboration with Data Enablement and DevOps teams.
Applying DevOps principles to QA workflows, especially in cloud-native environments using AWS (e.g., S3, Glue, Athena, Step Functions, Lambda, CloudFormation).
Some other highly valued skills may include:
Experience with LangChain, Vector DBs (e.g., OpenSearch, PG vector, ChromaDB), and RAG pipelines.
Understanding of model interpretability, responsible AI, and governance frameworks.
Strong leadership, communication, and cross-functional collaboration skills.
Knowledge of data engineering, data privacy, model governance, and regulatory compliance in AI systems.
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 for Pune location.
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