---
title: "Aditya Jangra - Program Analyst in Hyderabad | Tal"
description: "Meet Aditya Jangra, Computer Vision & ML Engineer | Process Automation & Client-Facing Delivery | SQL • OpenCV • TensorFlow • ServiceNow based in Hyderabad. AI stack includes antigravity, claude code, and claude. Explore public work links including github."
image: "https://tal.club/meet/aditya-jangra/share-image"
---

# Aditya Jangra on Tal

## Headline

Computer Vision & ML Engineer \| Process Automation & Client\-Facing Delivery \| SQL • OpenCV • TensorFlow • ServiceNow

## About

I build production\-ready Computer Vision and Machine Learning systems that integrate into real products, not just research prototypes\. My work focuses on designing end\-to\-end CV and ML pipelines, from data processing and model development to API\-based deployment and integration\. Alongside my CV/ML work, I bring hands\-on professional experience in enterprise process automation and stakeholder management — currently at Cognizant, where I automate approval and change\-management workflows and coordinate cross\-functional rollouts end\-to\-end\. This gives me a practical, delivery\-focused approach on top of my technical background: I'm as comfortable gathering requirements and managing stakeholder sign\-off as I am building the underlying system\. What I work on: Computer Vision systems \(Facial Recognition, Gesture Recognition, OCR\) Deep Learning models using OpenCV and TensorFlow Time\-series and sequence modeling \(LSTM\-based systems\) Applied GenAI solutions \(RAG pipelines, LLM\-powered document intelligence\) REST APIs for real\-time inference and system integration Representative work includes: A high\-accuracy facial recognition system using Siamese Neural Networks A real\-time American Sign Language recognition system using spatiotemporal modeling An LLM\-powered document intelligence system for querying large document collections I currently collaborate with startups, product teams, and founders who need custom Computer Vision or ML solutions that are reliable and deployment\-ready\. 📩 If you’re building a product involving images, video, or document automation, feel free to reach out with your use case\.

## Location

Hyderabad

## AI stack

antigravity

claude code

claude

codex

hugging face

Blackbox AI

## Work experience

Program Analyst at Cognizant, Jun 2025 \- Present · 1 yr 4 mos, Hyderabad, Telangana, India, • Own end\-to\-end automation of ServiceNow approval workflows for change requests and incidents, coordinating directly with business and engineering stakeholders to secure timely sign\-offs\. • Plan and execute software upgrade rollouts across multiple production servers — scheduling, validation, and stakeholder communication through each phase of the rollout\. • Gather requirements from stakeholders for new features and process changes, then scope and implement the corresponding automation updates\. • Write Python scripts to automate repetitive data\-transformation and reporting tasks, improving throughput and accuracy of existing workflows\.

Computer Vision & Machine Learning Engineer at Freelance, Apr 2023 \- Present · 3 yrs 6 mos, Remote, 🔹 LLM\-Powered Document Intelligence System Tech: GenAI, LLMs, RAG, FAISS, Python, REST APIs Designed and implemented an end\-to\-end Retrieval\-Augmented Generation \(RAG\) system for querying large document collections Built document ingestion, chunking, and embedding pipelines using transformer\-based models and FAISS vector databases Enabled semantic search and contextual response generation through LLMs Exposed the system via REST APIs for real\-time inference and enterprise integration 🔹 Facial Recognition System \(Siamese Neural Networks\) Tech: Computer Vision, Deep Learning, OpenCV, TensorFlow Developed a high\-accuracy facial recognition and verification system using Siamese Neural Networks Engineered face embedding pipelines and similarity\-based identity verification logic Optimized preprocessing and model tuning to achieve 98% accuracy Designed the system for real\-world identity verification use cases 🔹 American Sign Language \(ASL\) Recognition System Tech: OpenCV, MediaPipe, LSTM, Time\-Series ML Built a real\-time ASL gesture recognition system using spatiotemporal hand landmark sequences Applied LSTM\-based models for dynamic gesture recognition Achieved 97% accuracy in real\-time classification Optimized the pipeline for low\-latency inference

## Public links

- github: https://github\.com/aditya\-0114

[View the original profile](https://tal.club/meet/aditya-jangra)

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