Background.
Professional history, education, and the technical scope of my work. A downloadable CV is available when a formal document is useful.
Professional history.
Production software across 12+ years, including 7+ years building and deploying AI systems.
Associate Technical Architect · KPIT Technologies
Architects AI systems across automotive perception, in-cabin intelligence, model industrialization, and the platforms that carry them into production.
- Leads technical direction from feasibility and architecture through C++ integration, optimization, and validation on target hardware.
- Defined a multi-model in-cabin system spanning whole-body pose, gaze, and temporal action and skeleton forecasting on NVIDIA Jetson.
- Developed reusable deployment patterns across NVIDIA DRIVE and Jetson, Qualcomm Snapdragon Ride, Ambarella, and Renesas platforms.
- Enabled deep-learning workloads on constrained automotive ECUs through runtime selection, custom operators, quantization, graph optimization, and Python-to-C++ redesign.
- Built perception middleware and vehicle interfaces across camera, LiDAR, radar, traffic-light recognition, tracking, and concurrent inference.
- Leads MLOps and DataOps architecture, and advises internal teams on agent use cases, evaluation, orchestration, tool integration, state and memory, and human approval.
Earlier work
- Software Engineer · Independent / Freelance Work2017 - 2018 · Bengaluru
Built product-oriented ML and web systems before moving fully into production AI.
- Software Engineer · Accenture2014 - 2017 · Bengaluru
Worked on backend and telecom platforms where automation, migration work, and production correctness mattered.
Education and technical scope.
Education and the technical scope I work across most often.
Education
- M.S. Computer Science
University of Colorado Boulder
In progress · GPA 4.0 - Executive PG Programme in ML & AI
IIIT Bangalore - B.E. Computer Engineering
Nagpur University
Patent
- Mobile Device Performance Improvement System Using Cloud Computing
Indian patent application 1922/MUM/2013
Technical scope
Languages
Python, C++, Rust, C
ML / CV
PyTorch, TensorFlow, ONNX, ONNX Runtime, TensorRT, OpenCV, scikit-learn
Deployment
Model optimization, Quantization, Distillation, Graph optimization, Inference profiling, Embedded pipelines
Domains
Perception systems, Autonomous driving, In-cabin sensing, Driver monitoring, Multi-modal pipelines, Agent infrastructure, Agent evaluation, Technical architecture