1. Contribute to the System Integration & Validation (SIV) team supporting large-scale system bring-up, verification, and end-to-end validation of autonomous vehicle technology and robotic platforms.

2. Design, develop, and validate automated test frameworks enabling seamless hardware–software integration, diagnostic verification, and regression automation for vehicle systems.

3. Conduct system-level debugging, telemetry analysis, and data correlation to identify and resolve hardware, firmware, and software issues impacting vehicle behavior and real-time performance.

4. Utilize internal engineering tools including Zalender, Overwatch, Riderdash, Telegraph, and Teledash for system monitoring, telemetry visualization, and performance analytics.

5. Coordinate workflow management and issue resolution through JIRA, Google Sheets, and Excel, maintaining traceability from test execution to production deployment.

6. Collaborate with cross-functional hardware, software, and QA teams via Slack to align integration milestones, perform root-cause analysis, and improve test efficiency.

7. Apply advanced Computer Science principles including software engineering, AI, distributed computing, and algorithm optimization to enhance automation, data reliability, and validation throughput.

8. Drive process optimization initiatives to strengthen operational efficiency, expand test coverage, and accelerate validation of complex autonomous vehicle subsystems.

1. Spearheaded development of AI-Enhanced Treasury Management Insights, leveraging predictive analytics and machine learning to improve cash flow forecasting and risk detection for corporate clients.

2. Implemented FastAPI and Python-based microservices integrated with PostgreSQL and Firebase, enabling scalable real-time data processing.

3. Utilized HuggingFace Transformers, Scikit-learn, and LLMs (GPT, Claude, BERT) to deliver advanced anomaly detection and trend analysis.

4. Deployed solutions on AWS with Docker and Kubernetes, while integrating Prometheus and Grafana for monitoring and system observability.

5. Partnered with treasury stakeholders in an Agile/Scrum environment, ensuring AI-driven insights aligned with evolving financial strategies and compliance standards.

1. Engineered and modified program logic for new and existing fintech applications using Java, Spring, JAX-RS, and Python, supporting the full systems development life cycle (SDLC).

2. Evaluated reports and data extracts to ensure compliance with business requirements and regulatory standards, maintaining data integrity through detailed analysis and problem-solving.

3. Optimized ETL workflows with Apache Airflow, ensuring timely pipeline execution, 100% uptime, and uninterrupted financial reporting.

4. Directed QA efforts for the Modern Banking Platform, cutting software defects by 20% through rigorous functional and non-functional testing and root cause analysis.

5. Executed backend, upgrade, and automated regression tests, boosting efficiency by 15% and reducing time-to-market by 10%.

6. Authored detailed test cases, scripts, and plans, ensuring complete traceability and robust validation across multiple product releases.

7. Guided junior developers by providing mentorship on coding standards, debugging strategies, and best practices, ensuring alignment with project goals.