Jaykumar Gupta

AI Researcher

Thane, Maharashtra, India4 yrs 5 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Stackforce AI infers this person is a Machine Learning Engineer with expertise in Fintech and SaaS industries.

Machine LearningArtificial Intelligence (ai)Full-stack DevelopmentData Science

AWSBusinessComputer VisionData AnalysisDesignEngineeringFastAPIGCPGenAILeadershipML solutionsMicrosoft ExcelNLPOCRPostgreSQL

M.tech ||Machine learning, Deep learning and Data Science Enthusiast|| IIT Bombay

Jun 2023 – Present · 2 yrs 9 mos · Mumbai Metropolitan Region · On-site

Key Responsibilities
Design and deploy end-to-end GenAI and ML solutions across departments such as Compliance, Marketing, HR, and Investment Strategy.
Build scalable RAG pipelines, agentic bots, and voice-to-text analytics workflows using AWS & open-source LLMs.
Automate business processes using cloud-native services (AWS , GCP).
Interface with stakeholders to gather requirements, demo solutions, and continuously iterate based on feedback.
Lead full-stack development (React + FastAPI + PostgreSQL) for AI-powered internal applications.
Fine-tune and integrate large language models (LLMs) LLaMA and deploy with tools like LangChain, LlamaIndex, and LangGraph.
Tech Stack & Tools
GenAI & NLP: GPT, Claude, LLaMA, LangChain, LlamaIndex, LangGraph, Crew AI, RAG, Prompt Engineering
Cloud & Infra: AWS (Bedrock, SageMaker, Textract, Transcribe, Lambda, ECS,ECR, load balancers,EC2 Step Functions, S3,Vertex AI , notebook LM,big Query)
Programming: Python, FastAPI, React, HTML/CSS, Git, Postman
Database & Vector: PostgreSQL, Vector DBs, ChromaDB
OCR & NLP: Textract, spaCy, NLTK

GenAIML solutionsAWScloud-native servicesfull-stack developmentlarge language models+2

Jul 2021 – Mar 2023 · 1 yr 8 mos · Pune, Maharashtra, India

Key Responsibilities
Designed and deployed scalable machine learning workflows and cloud-native data solutions for clients in banking, infrastructure, and enterprise domains.
Built modularized ML pipelines using AWS , GCP, and open-source tools to enable production-ready deployments.
Collaborated with clients for requirement gathering, experimentation, and iterative delivery of ML-based products.
Integrated cloud OCR and NLP tools (e.g., Google Vision API) for intelligent document processing.
Participated in end-to-end solution ownership: from model training to API deployment and automation.
Tech Stack & Tools
ML & DS: SageMaker, K-Prototypes, U-Net, Scikit-learn, PyTorch, TensorFlow, EDA, clustering
Cloud & OCR: AWS Fraud Detector, SageMaker, Google Vision API
Languages & Tools: Python, Pandas, NumPy, FastAPI, Git, Postman
ML Workflow: Modular Pipelines, Data Cleaning, Feature Engineering, Batch Inference, Model Tuning
Domains: Banking, Infrastructure, Document Digitization