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Open to AI Engineer / ML Engineer opportunities · Ahmedabad

Hi, I'mYashpal SinghAI/ML R&D Engineer4 Years of Professional Experience

Building production AI products and intelligent automation systems.

AI/ML R&D Engineer with 4 years of professional experience. Focused on Retrieval-Augmented Generation, LLM applications, and computer vision, using modern AI tooling to take projects from research to production.

PythonFastAPILangChainPyTorchRAGLLMs
4Years Experience
1Production Project
15+Technologies
8AI Features Built
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Career

Experience

AI/ML R&D Engineer with 4 years of professional experience — model integration, RAG systems, and computer vision.

  • Designed and built Retrieval-Augmented Generation (RAG) systems, combining LLM workflows with vector search to ground responses in retrieved context.
  • Built and experimented with model training, fine-tuning, and inference workflows using PyTorch and Hugging Face.
  • Built computer vision solutions using OpenCV and YOLO-based models for object detection and image-processing tasks.

Stack

PythonPyTorchHugging FaceLangChainVector DatabasesOpenCVYOLOFastAPI
Featured Work

AI Projects

Production systems built end-to-end — from dataset curation to deployed APIs.

Independently BuiltAI-PoweredLive in Production
👑
Live2026

CVKing

AI-Powered Resume & Career Platform

An AI-powered resume and career platform, independently designed, built, and deployed end-to-end — frontend, backend, database, AI integrations, and infrastructure all handled solo. Live in production and actively used by real users for the complete job-application workflow, from a blank form to a downloadable, recruiter-ready PDF.

🤖

AI Resume Optimizer

Rewrites bullets with stronger action verbs and impact framing.

ATS Score & Formatter

Automatically formats and scores resumes to pass ATS filters.

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Live Preview Editor

Resume updates render in real-time as users type — no reloads.

Next.jsJWT AuthbcryptRazorpayGoogle OAuth
Live Demo
RAG PipelineSelf-HostedModular Design
🧠
Open Source

Billo AI

Retrieval-Augmented Generation (RAG) Pipeline

An end-to-end document-intelligence pipeline that ingests PDFs, generates embeddings, and answers questions over private documents — built as two decoupled services, not a single notebook script.

🧩

Modular Architecture

Ingestion and retrieval run as independent, decoupled stages.

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Chroma Vector Persistence

Local vector store — no managed DB dependency.

FastAPI Backend

Typed, async REST endpoints for ingestion and query.

PythonFastAPILangChainChromaDBSentence-Transformers
View on GitHub
Capabilities

Skills & Stack

AI/ML core, with full-stack range to build and ship it end-to-end.

🤖

AI / ML

PythonTensorFlowPyTorchMachine LearningDeep Learning

LLM & AI

LangChainRAGHugging FaceFine-Tuning
👁️

Computer Vision

OpenCVYOLO
📊

Data

PandasNumPySQL
⚙️

Development

FastAPIGitLinux
System Design

AI Architecture Showcase

How I think about end-to-end ML systems — from raw data to monitored production.

📥

Data Layer

Ingestion & Processing

Sources

APIs, databases, files, streams

Processing

Cleaning, validation, feature engineering

Versioning

DVC data pipelines + S3 storage

Monitoring

Schema validation + drift detection

Tools

DVCPandasApache KafkaS3Great Expectations
Get in Touch

Let's Work Together

Open to AI Engineer / ML Engineer opportunities. Let's talk.

Available Now

Open to AI Engineer / ML Engineer opportunities

Actively looking for AI/ML Engineering roles. Response time: <24h.