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Staff ML Engineer | LLMs, RAG, Document AI | MLOps & ML Infrastructure | AWS | CMU
I'm a Staff ML Engineer at Automation Anywhere, where I've spent 7+ years building production ML systems that help make sense of unstructured data. Most of my recent work focuses on RAG systems and using vision-language models for document understanding at scale. I've also led our MLOps initiative, bringing in best practices for model lifecycle management and implementing infrastructure that supports active learning and continuous retraining workflows on AWS. I've been fortunate to contribute to several patents along the way, and these days I spend a good amount of time helping teams navigate the practical challenges of getting ML models into production.
Carnegie Mellon University
Master of Science (M.S.), Electrical and Computer Engineering
N/A – Present
Savitribai Phule Pune University
Bachelor of Engineering - BE, Electronics and Telecommunications Engineering
N/A – Present
Automation Anywhere
Staff Machine Learning Engineer
February 1, 2024 – Present
San Francisco Bay Area
Automation Anywhere
Senior Machine Learning Engineer
February 1, 2021 – January 1, 2024
San Francisco Bay Area
Automation Anywhere
Machine Learning Engineer
February 1, 2018 – February 1, 2021
San Francisco Bay Area
LG Electronics
Image Processing/ Deep Learning Research Intern
June 1, 2017 – August 1, 2017
Santa Clara, California
Carnegie Mellon University
Graduate Research Assistant
January 1, 2017 – May 1, 2017
Greater Pittsburgh Area
IUCAA, Pune, India
Project Intern
June 1, 2015 – June 1, 2016
Pune Area, India
HTTP/1.0 Web Proxy
December 1, 2016 – Present
Implemented a HTTP/1.0 Web proxy for serving multiple, concurrent HTTP/1.0 GET requests. Optimized it by designing a cache to store recently accessed web objects with a least recently used eviction policy and utilized to concurrency for faster loading of webpages. Implemented mutexes for employing readers-writers lock.
Dynamic Memory Allocator (Malloc)
November 1, 2016 – Present
Implemented a dynamic memory allocator in C with high memory utilization and throughput for handling malloc, calloc, realloc and free requests. Utilized segregated free list (doubly linked list) with suitable bucket size selection for improving throughput while the use of boundary tag coalescing ensured proper memory utilization. The use of first fit search for free blocks facilitated in improving throughput while the elimination of footers while allotting memory block helped in reducing internal fragmentation.
Super-Resolution for facial images using Deep Convolutional Networks
September 1, 2016 – November 1, 2016
Implemented a Convolutional Neural Net to construct a high-resolution image from a given close-up face image. Compared the results with resolution done just with bicubic interpolation and concluded that for higher upscaling factors a CNN gives much better results than bicubic interpolation. Observed that CNN trained on random images gives better results than a one trained using just facial images.
GSM based Home Security
January 1, 2015 – April 1, 2015
This project dealt with the design and development of a theft and fire detection system for homes. The security system comprised of a Atmega328 micro-controller (based on an Arduino platform) along with a PIR sensor and a SIM900A based GSM modem. The PIR sensor was used to detect human presence and/ or fire by capturing Infrared radiation while the GSM modem sent messages to the nearest police station as well as the home owner.
AWS Certified Solutions Architect – Associate
Amazon Web Services (AWS)
June 24, 2026 – Present
AWS Certified Cloud Practitioner
Amazon Web Services (AWS)
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's experience is heavily skewed towards Machine Learning, Deep Learning, and Computer Vision roles, which is not a direct match for a Data Analyst target role. While there is a project involving astrophysics data analysis, the primary career trajectory has been in ML engineering. The AWS certifications indicate a breadth of interest in cloud technologies, but the overall profile suggests a strong preference and expertise in ML/AI development rather than core data analysis, dashboarding, or business intelligence. This indicates a potential mismatch with a pure Data Analyst role, though the analytical skills are transferable.
Soft Skills & Operational Fit
The candidate's project descriptions indicate strong problem-solving abilities and a structured approach to complex technical challenges. The detailed explanations of design choices (e.g., segregated free list, boundary tag coalescing) suggest a deep understanding and analytical mindset. However, without psychometric test results or interview data, it is difficult to assess specific soft skills like teamwork, leadership, or stress handling.