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The Senior AI Engineer will design, develop, and operate AI-powered software systems, focusing on practical implementation and collaboration with various teams to enhance AI usage in software development.

Lead development and maintenance of scalable test automation frameworks and scripts for UI, API, SIT, and E2E testing. Integrate test suites into CI/CD pipelines, optimize test reliability, produce test reports and logs, participate in defect triage, and mentor junior testers while collaborating with developers, QA, and DevOps to ensure release quality.

Design, develop, and optimize PL/SQL packages, procedures, and ETL/data-load routines for an Operational Data Store. Perform SQL/PLSQL performance tuning and data modeling, ensure data quality and reconciliation, troubleshoot production issues, and collaborate with business and technical teams to translate requirements into database solutions. Produce technical documentation and test cases.

Design, develop, and support enterprise business process workflows using IBM BAW/BPM. Model BPMN 2.0 processes, integrate with core banking systems via REST/SOAP, configure WebSphere/Liberty environments, perform testing and performance tuning, support UAT and production, and produce technical documentation.

Design, build, and support enterprise integration solutions using IBM ACE/IIB and IBM MQ to connect core banking and peripheral systems. Develop message flows, ESQL/Java transformations, and integrations over MQ, REST, SOAP, JMS, and file protocols. Perform tuning, capacity planning, incident diagnosis, and documentation for production middleware.

Design, build, and maintain scalable microservices and APIs using Spring Boot or Quarkus; manage APIs with IBM API Connect; ensure performance, security (OAuth2/JWT), and observability; containerize and deploy on Docker, Kubernetes/OpenShift; write unit/integration/contract tests; collaborate with product, QA, and DevOps; troubleshoot production issues and support CI/CD pipelines.

Design, develop, and deploy machine-learning and time-series forecasting solutions. Perform feature engineering, model validation, backtesting, and production monitoring. Build scalable data pipelines, dashboards, and collaborate with stakeholders while mentoring junior team members.

Lead design and delivery of production-grade time-series forecasting for financial use cases: architect pipelines, develop and benchmark classical and ML models, implement time-aware validation and Bayesian tuning, perform backtesting, quantify uncertainty, deploy and monitor models with MLOps, build dashboards, and mentor junior engineers while collaborating with finance and risk stakeholders.

Design and optimize ETL pipelines and data models, translate legacy SAP BusinessObjects reports into Power BI, build automated executive dashboards, apply banking domain analytics to track metrics and assess risk, and tune performance for large datasets.

Design, develop, and operate AI-powered software systems focused on practical implementation. Build and optimize prompts, create data pipelines, fine-tune/apply RAG, automate LLM workflows/agents, and produce data visualizations from financial datasets for production use.