Artificial Intelligence Engineer

Cognizant Technology Solutions Corporation; Greater London; Hybrid; GBP 110,000 - 150,000; Full time

Job summary

Cognizant Technology Solutions Corporation is seeking an AI Engineer to design, develop and deploy AI solutions into production, spanning LLMs, agentic frameworks and predictive algorithms for digital payments.

You will blend software engineering with AI expertise to build scalable, cost-efficient systems across cloud and edge environments, improving fraud detection, customer experiences and transaction processing, while championing best practices with Databricks.

Qualifications

• Experience building production-grade AI/ML solutions with reliability and observability.
• Hands-on data engineering to prepare data for model training and real-time inference using cloud services.
• Strong knowledge of security, scalability, and governance in AI systems.

Responsibilities

• Design end-to-end AI architectures connecting cloud services and payment platforms with reliability and resilience.
• Ingest, transform and curate data for model training and real-time inference using Azure tools.
• Develop LLM-powered and agentic solutions for customer assistance and documentation generation.
• Apply ML techniques (supervised/unsupervised/reinforcement) to payments risk, fraud, and txn optimisation.
• Build CI/CD workflows for automated testing, deployment and monitoring across hybrid infrastructure.

Skills

Azure Databricks

Azure Data Factory

Jenkins

ML/AI engineering

Edge computing

Tools

Jenkins CI/CD

Job description

Ready to move beyond experiments and build AI that performs in the real world? We’re looking for an AI Engineer to design, develop and bring intelligent solutions into production—from large language models (LLMs) and agentic frameworks to predictive algorithms.

You’ll combine strong software engineering with deep AI expertise to build scalable, cost-efficient and reliable systems for digital payments. Working across cloud and edge environments, you’ll help improve fraud detection, customer experiences and transaction processing, while championing best-practice use of Databricks.

Why this opportunity stands out

Bring together architecture, hands-on engineering and real-world impact. You’ll tackle demanding payments challenges, turn emerging AI techniques into practical capabilities, and share your expertise to help the wider team deliver better solutions.

What you’ll do:

• Design AI from end to end: build autonomous edge AI architectures connecting cloud services and payment platforms, with security, availability and resilience built in.
• Build the data foundations: use Azure Data Factory and Azure Databricks to ingest, transform and curate structured and unstructured payment data for model training and real-time inference.
• Turn generative AI into useful products: develop LLM-powered and agentic solutions for customer assistance, anomaly explanation and documentation generation.
• Apply machine learning to payments: use supervised, unsupervised and reinforcement learning to develop predictive and prescriptive capabilities for risk management, fraud detection and transaction optimisation.
• Engineer for production: build Jenkins CI/CD workflows for automated testing, deployment and monitoring across hybrid infrastructure; optimise edge inference for low latency and high throughput.
• Prove and monitor performance: run experiments, historical-data backtesting and A/B evaluations to assess fraud reduction, customer satisfaction and process efficiency. Implement logging and observability to identify model drift, data-quality issues and operational incidents.
• Build responsibly: partner with payments experts, product managers, data, cybersecurity and compliance teams to translate business requirements into scalable solutions, embedding encryption, access controls, privacy, explainability and auditability.
• Strengthen the team: document architectures, data models, pipelines and operational processes; mentor colleagues in Azure Databricks, Azure Data Factory, Jenkins and AI engineering practices.
• Keep improving: evaluate emerging generative AI, edge inference and MLOps approaches, recommending practical enhancements that improve resilience, business value and trust in digital payments.

What you’ll bring:

• Strong hands-on experience designing, developing and operating production-grade AI and ML solutions, with a focus on reliability, scalability, observability and maintainability.
• Extensive experience with Azure Databricks and Azure Data Factory, spanning large-scale data engineering, model development and production operations.
• Practical proficiency in Jenkins for CI/CD pipelines, automated testing and safe, repeatable releases across hybrid environments.
• A deep understanding of feature engineering, model selection, hyperparameter tuning and evaluation metrics, applied to payments risk and fraud use cases.
• Proven experience building generative AI applications, including conversational agents or content-generation tools integrated with payment platforms, with attention to accuracy, explainability and responsible data use.
• Solid payments-domain knowledge, 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 during day-shift operations.