1 day ago
London, United KingdomSenior
Responsibilities
- Design end-to-end cloud and edge AI architectures integrated with payment platforms, incorporating security, availability, and resilience.
- Use Azure Data Factory and Azure Databricks to ingest, transform, and curate structured and unstructured payment data for model training and inference.
- Develop LLM-powered and agentic solutions for customer assistance, anomaly explanation, and documentation generation.
- Build supervised, unsupervised, and reinforcement learning capabilities for payments risk management, fraud detection, and transaction optimization.
- Create Jenkins CI/CD workflows for automated testing, deployment, monitoring, and repeatable releases across hybrid infrastructure.
- Optimize edge inference for low latency and high throughput and implement logging and observability for model drift, data quality, and operational incidents.
- Evaluate AI system performance through experiments, historical-data backtesting, and A/B evaluations.
- Embed encryption, access controls, privacy, explainability, and auditability into AI solutions.
- Document architectures, data models, pipelines, and operational processes while mentoring colleagues in AI engineering practices.
Requirements
- Strong hands-on experience designing, developing, and operating production-grade AI and ML solutions with emphasis on reliability, scalability, observability, and maintainability.
- Extensive experience with Azure Databricks and Azure Data Factory across data engineering, model development, and production operations.
- Practical proficiency with Jenkins for CI/CD pipelines, automated testing, and safe releases across hybrid environments.
- Strong understanding of feature engineering, model selection, hyperparameter tuning, and evaluation metrics applied to payments risk and fraud use cases.
- Experience building generative AI applications, including conversational agents or content-generation tools integrated with payment platforms.
- Knowledge of payments domains including transaction flows, settlement, chargebacks, fraud patterns, and regulatory considerations.
- Clear communication and structured documentation skills, with experience collaborating across distributed teams and hybrid environments.
Tech Stack
Categories
About Cognizant
Cognizant is a public IT services and consulting firm that designs, builds, and runs enterprise technology, including digital engineering, cloud modernization, data/AI, and managed services. It sells consulting, systems integration, and outsourcing on multi-year engagements to large enterprises in healthcare, banking, retail, communications, and manufacturing. Founded in 1994 and headquartered in Teaneck, New Jersey, Cognizant is NASDAQ-listed (CTSH) and a Fortune 500 company.