Riddhesh

Software and Data Engineer with 2+ years of commercial experience building high-performance automation frameworks, machine learning pipelines, and data-driven applications in banking, insurance, and AI research. Proficient in Python, SQL, and PyTorch, with a proven track record of improving system response times by 25%, maintaining sub-3-second SLAs at 20,000+ concurrent users, and delivering object detection models at 85%+ accuracy. Seeking a software or data engineering role where robust engineering and applied ML intersect.

Data & Analytics Engineer - Seabed.AI - University of Greenwich StEP Programme

(2024-10 - 2025-08)

• Researched and analysed domain-specific sonar datasets to identify performance gaps in object detection models, directly informing the team's model development roadmap.
• Designed and validated a domain-adapted object detection pipeline in PyTorch using transfer learning; achieved 85%+ accuracy across held-out test sets, exceeding the project's target benchmark.
• Standardised data formatting conventions and processed data change requests end-to-end, significantly improving research reproducibility and enabling faster model iteration cycles.
• Collaborated with academic researchers and software engineers in an Agile environment, presenting findings at regular sprint reviews.

Software Test Engineer - QualityKiosk Technologies Pvt. Ltd.

(2022-06 - 2024-07)

• Built and maintained Python-based automated test execution frameworks for core banking and insurance platforms, reducing regression cycle time and consistently achieving SLAs of <3 s (web) and <1 s (API).
• Designed and analysed performance testing strategies using Apache JMeter, simulating 20,000+ concurrent users; identified and resolved bottlenecks that improved overall application response time by 25%.
• Developed comprehensive load, stress, and endurance test suites covering REST APIs and web services, providing actionable reports to development teams across two major product lines.
• Created Power BI and Excel dashboards for insurance clients to visualise real-time performance metrics, accelerating root cause analysis and reducing incident resolution time.
• Worked closely with developers, product owners, and client stakeholders to translate business requirements into robust test plans and acceptance criteria.

MSc - Data Science - University of Greenwich (2024-09 - 2025-10)

B.E. - Information Technology - Atharva College of Engineering (2018-08 - 2022-05)