San Francisco, CA · Full-stack AI engineer
Engineering Intelligence.
I build AI systems that work in the real world — from LLM agents, Computer Vision models to the full-stack products that put them to use.
About
The engineer behind the work.
The motivation and mindset behind what I build.
I build production AI systems — LLM agents and Computer Vision models delivered as full-stack software products.
My experience spans fast-moving software startups, vision and software solutions for Atlas Copco, and robotics for manufacturing.
I have delivered scalable backend systems, cloud infrastructure, and agentic workflows in production.
I enjoy taking AI products from 0 to 1, integrating them into existing workflows and eventually deploying them into real-world production.
Quick facts
- Currently
- Graduate AI/ML Research Assistant · Trustworthy Autonomous Systems Lab, UC Riverside
- Education
- M.S. Computer Science, UC Riverside
- Based in
- San Francisco, California
- Focus
- LLM agents, RAG pipelines, perception, full-stack systems
Experience
Shipping reliably in Production.
Internships and Research roles — from the labs to startups and factory floors
Graduate AI/ML Research Assistant
Apr 2025 — May 2026Trustworthy Autonomous Systems Lab, UC Riverside
Riverside, CA · Research
- Designed a multi-modal analytical framework fusing Vision-Language Models (VLMs) with spatial data features to predict anomaly-intent thresholds across complex variables (submitted to IROS 2026).
- Achieved 20% faster model convergence in LLMs vs. baseline statistical trends by integrating historical variance and feature-engineering data into the reasoning context.
- Mitigated operational risk by 30% with a strict confidence-modulated governance protocol that shares execution controls between automated models and manual validators.
Software Engineer
Jan 2024 — Jun 2024Digital Vision Studios
Pune, India · Internship
- Engineered and delivered a high-concurrency ledger and secure transactional data system for 5,500+ active users under strict data-governance, integrity, and risk-mitigation standards.
- Orchestrated a migration to a hybrid cloud architecture (AWS / DigitalOcean), scaling capacity to process 8,000+ daily analytical requests.
- Automated workflows and built deployment guardrails with Docker, reducing delivery cycles from 2 hours to 15 minutes.
Software Engineer
Jun 2023 — Dec 2023Atlas Copco
Pune, India · Internship
- Led the analysis, design, and delivery of an AI vision pipeline using YOLOv8 to validate manufacturing operations.
- Achieved 92.5% defect-detection accuracy while reducing per-unit inspection time from 45 seconds to under 2 seconds.
- Automated handwritten-checklist tracking with multimodal AI (image + text), reducing per-checklist processing from 5 minutes to under 1 minute (80% reduction).
- Built a Dockerized MLOps pipeline and partnered with operations teams to deliver a full-stack warehouse management system (Django & React.js).
ML Research Assistant (Computer Vision, ML)
Aug 2022 — May 2023Vishwakarma Institute of Information Technology (VIIT)
Pune, India · Research
- Conducted applied research on robust facial detection and recognition for occluded and profile faces using Convolutional Neural Networks and Haar Cascade classifiers.
- Engineered a deep-learning pipeline spanning image preprocessing, patch splitting, and EDSR-GAN super-resolution, improving detection accuracy to 93.2%.
- Published and presented the work at the Scopus-indexed 9th ICICT 2024, London (Springer Nature).
Research
Publications over the years.
My research focuses on VLMs, VLAs, Perception for Robotics and Autonomous vehicles. I have published one first-author paper in Springer Nature journal and submitted one to IROS 2026. My work enables agents to understand, navigate, and act in real-world environments.
SATeMoMa: A Safe Assistive Teleoperation System for Mobile Manipulation
Submitted to IROS 2026
A system that reduces operator workload during mobile manipulation. It predicts user intent using a vision-language model, spatial features, motion patterns, and speech, and estimates its confidence in each prediction. Based on that confidence it switches between teleoperation, shared control, and autonomous assistance, while Control Barrier Functions ensure safe, collision-free motion. In simulation it achieved an 86.7% task success rate with zero collisions.
Facial Detection and Recognition of Partially Occluded and Profile Faces
9th ICICT 2024, London · Springer Nature (Scopus-indexed)
A pipeline that improves face recognition for profile views, partially occluded faces, and low-quality CCTV footage. Images are first enhanced with patch-based super-resolution and sharpening; faces are detected with Haar Cascade classifiers; and a CNN trained on multi-angle faces with occlusion-based augmentation recognizes identities. The system achieved 93.2% detection and 96% recognition accuracy.
Technical Stack
Tools behind the outcomes.
The technologies I reach for across AI, machine learning, and full-stack systems.
Languages
The languages I think and build in.
Milestones
Selected highlights.
IROS 2026 Submission
Co-authored a multi-modal VLM + spatial-data framework for anomaly-intent prediction, submitted to IROS 2026.
M.S. in Computer Science
Completed a master's at UC Riverside focused on AI, ML, reinforcement learning, and NLP.
Cloud & DevOps Certified
Earned AWS Certified Cloud Practitioner plus hands-on Kubernetes and Docker DevOps certifications.
92.5% Defect Detection
Shipped a YOLOv8 vision pipeline at Atlas Copco, cutting per-unit inspection from 45s to under 2s.
Contact
Let's build useful AI.
Open to AI engineering roles, applied-research collaborations, and production AI systems that need both model depth and product execution.
Start a conversation
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