Ajit J Gupta

Software Engineer

Bengaluru, Karnataka, India1 yr 10 mos experience

Key Highlights

Expert in Machine Learning and Computer Vision.
Proven track record in optimizing data pipelines.
Strong experience with AWS architecture and deployment.

Stackforce AI infers this person is a Machine Learning Engineer with expertise in Computer Vision and AWS architecture.

Contact

Skills

Core Skills

PythonPysparkComputer VisionAwsMachine LearningFlask

Other Skills

EngineeringSoftware DesigntypescritAWS CloudFormationFastAPIAutoencodersPython (Programming Language)JavaMongoDBOptical Character Recognition (OCR)pytestDatadogdatabricksNestJSApache Airflow

About

I pursued EEE from BMS College of Engineering, Bangalore. I worked on multiple ML projects in collaboration with Dassault Systèmes and Samsung PRISM. I also completed an internship with a start-up, where I worked with a tech stack that included OpenCV, NLP, and Flask. At Hinge Health, I worked on projects involving AWS at the architectural level, deploying services on ECS and creating workflows using CloudFormation Templates (CFT). Currently, I am part of the Growth team, where I facilitate data for the marketing teams using Python and Databricks.

Experience

1 yr 10 mos

Total Experience

1 yr 10 mos

Average Tenure

1 yr 10 mos

Current Experience

Hinge health

3 roles

Software Engineer 2

Promoted

Mar 2026 – Present · 2 mos

EngineeringSoftware Design

Software Engineer-1

Jul 2024 – Mar 2026 · 1 yr 8 mos

Designed and optimized PySpark pipelines to transform large-scale event data from raw to curated layers, enabling real-time marketing automation via Iterable.
Replaced a 10-minute batch job (~$600/month cost) with an optimized PySpark streaming solution, reducing latency by 90% and saving 60% in monthly costs.
Orchestrated batch and streaming ETL workflows using Apache Airflow, with SLA tracking, retries, and dependency management.
Wrote unit tests using Pytest, ensuring high test coverage and reliability for core video processing and batch tracking components.

PythontypescritPySpark

Software Engineer Intern

Aug 2023 – Jun 2024 · 10 mos

Developed and maintained a FastAPI-based computer vision microservice that processed 10,000+ exercise videos weekly, enabling near real-time batch tracking across AWS ECS environments.
Integrated third-party CV engine (wrnch) for pose estimation and built a scalable parallel worker architecture, reducing video processing time through optimized batch sizing.
Architected scalable AWS infrastructure using CloudFormation, supporting multi-version CV engine deployment with auto-scaling ECS clusters handling up to 500 concurrent jobs during peak loads.

PythonAWS CloudFormationComputer VisionAWS

Indian institute of science (iisc)

ML Research Intern

Jun 2023 – Jul 2023 · 1 mo · Bengaluru, Karnataka, India · Hybrid

Worked on autoencoders and understood about neuromorphic computing and it's applications.

AutoencodersPython (Programming Language)Machine Learning

Samsung r&d institute india

Samsung Prism intern

Nov 2022 – May 2023 · 6 mos · Bengaluru, Karnataka, India · Remote

Built a machine learning model to predict the optimal WiFi access point, solving the “sticky band” problem by automatically switching to the best available network. Exposed the model via a Flask-based REST API for seamless client integration.
Developed an Android application (Java) to scan nearby WiFi networks and push structured signal data to MongoDB in real time for centralized processing.
Designed and implemented a backend ETL service to clean and transform raw WiFi scan data, extracting relevant features and preparing high-quality datasets for model training and inference.

Python (Programming Language)Machine LearningFlask

G-knowme

ML Intern

Nov 2022 – Dec 2022 · 1 mo · Bengaluru, Karnataka, India · Remote

Worked with OpenCV, OCR, NLP and Flask API during internship.

Optical Character Recognition (OCR)Flask

Dassault systèmes

Project Intern

Mar 2022 – Aug 2022 · 5 mos · Bengaluru, Karnataka, India

Funded Project titled : “Smart Aqua Shrimp farming using Intelligent swarm fish for
ecosystem monitoring and disease predictions”.
> Tested various transfer learning approaches to narrow down on a model for custom detection of
Shrimps, foreign objects. The model was also trained to identify existing diseases in shrimps.
> The mode was deployed on a local host computer that communicated with a Raspberry pi on the fish robot using socket programming to send live images from the under water shrimp farm.

Computer Vision