Implement reusable, production-grade audit software components.

Implement components of the system architecture for scalable audit automation and AI platforms, based on guidance from the Lead.

Build and maintain robust data pipelines and AI/ML workflows to support audit analytics and reporting.

Deploy and operationalize machine learning models with a focus on reliability, performance, and maintainability.

Ensure operational stability through CI/CD automation, system monitoring, and proactive troubleshooting.

Collaborate with Indias team.

Contribute to Buy vs Build evaluations by providing technical feasibility assessments and implementation effort estimates.

5 plus years of proven experience in cross-regional collaboration in the financial industry.

Bachelors or Masters degree in System Engineering, Computer Science, Data Engineering, Information Systems, or a related field.

Proficiency in Python, SQL, and one or more modern programming languages.

Experience with data pipeline orchestration tools, rest API and ETL frameworks (Snowflakes).

Strong Linux knowledge and familiar with both cloud and on local deployments

Strong network and cyber engineering knowledge (to allow for a reliable architecture)

Familiarity with ML model deployment.

Strong UI/UX development and deployment skills

Drive best practices in software development, testing, deployment, and documentation.

Understand and leverage Tradewebs technology stack to maximize integration and reduce the platform technology risk/debt.

Apply a pragmatic, ROI-driven approach to the technology selectionleveraging advanced solutions only when justified.

Promote a parsimonious approach to data load only retrieving and storing the information required.

Facilitate the compliance of the data stack with the Data Policies of Tradeweb.

Document system designs, deployment processes, and operational procedures for long-term sustainability.