profile-pic

Raghav Bansal

I’m a Machine Learning Engineer with almost 4 years at Samsung, building production-grade Machine Learning systems across on-device, cloud, and generative AI pipelines. I’ve worked on multilingual Natural Language Processing (NLP) models, Deep Learning, Retrieval-Augmented Generation (RAG) systems, and deployed Large Language Models (LLMs) at scale using Triton Inference Server and AWS.

My strengths include on-device AI (TensorFlow Lite, model quantization, unsupervised clustering), contextual search (Word2Vec, BERT), and building robust ML APIs integrated with Android and web services.

I’m currently open to opportunities focused on LLMs, on-device intelligence, Deep Learning, or scalable Machine Learning infrastructure.

  • Role

    Lead Engineer Machine Learning

  • Years of Experience

    4.3 years

Skillsets

  • Jenkins
  • model quantization
  • NLP
  • Ollama
  • Room
  • S3
  • SQS
  • Triton Inference Server
  • Bash
  • fine-tuning
  • MCP
  • Linux
  • NumPy
  • pandas
  • PostgreSQL
  • PyTorch
  • rag
  • Scikit-learn
  • HuggingFace
  • SLMs
  • Auto Scaling
  • Python
  • Java
  • SQL
  • AWS
  • Docker
  • Flask
  • Kotlin
  • MLOps
  • TensorFlow
  • Python
  • ChromaDB
  • EC2
  • FAISS
  • FastAPI
  • Gerrit
  • Git
  • Jira
  • LangChain
  • LLMs

Professional Summary

4.3Years
  • Mar, 2026 - Present 6 months

    Lead Engineer Machine Learning

    Samsung R&D Institute India
  • Jul, 2022 - Feb, 20263 yr 7 months

    Software Engineer Machine Learning

    Samsung R&D Institute India
  • Feb, 2022 - Jul, 2022 5 months

    R&D Intern Machine Learning Engineer

    Samsung R&D Institute

Work History

4.3Years

Lead Engineer Machine Learning

Samsung R&D Institute India
Mar, 2026 - Present 6 months
    Architected a multi-agent health orchestration system using MCP-based agents handling requests via WebSockets, reducing response latency by 30% while eliminating agent collisions through deterministic sequencing. Engineered PostgreSQL-backed session memory to persist context across 20+ turn interactions, improving clinical instruction recall by 40% and ensuring rigid agent boundaries. Implemented safety guardrails and custom hallucination checks to validate LLM tool calls in healthcare workflows, blocking 100% of non-compliant actions and ensuring safe tool-chain execution for critical queries.

Software Engineer Machine Learning

Samsung R&D Institute India
Jul, 2022 - Feb, 20263 yr 7 months
    Orchestrated a scalable LLM inference service on AWS EC2 via Triton Inference Server, utilizing custom AMIs and Auto Scaling groups to optimize compute costs by 20%. Engineered an asynchronous processing pipeline using SQS and S3, decoupling data ingestion to reduce end-to-end latency by 70% and increasing throughput by 3x. Engineered a hybrid semantic-search and autocorrect system by ensembling BERT contextual embeddings with Word2Vec and Doc2Vec, achieving 97% Top-k accuracy on complex queries. Productionized a 94-language polyglot NLU model served via FastAPI and integrated with Android (Retrofit), optimizing inference for real-time multilingual support. Architected a RAG pipeline using dense vector embeddings and GPT for contextual query generation, improving intent resolution rates by 30% via semantic grounding. Engineered an ultra-compact 719 KB on-device CNN for surface classification (98.8% accuracy), optimizing inference to run within 610 KB RAM & 10 MB ROM to drive adaptive haptic feedback.

R&D Intern Machine Learning Engineer

Samsung R&D Institute
Feb, 2022 - Jul, 2022 5 months
    Developed a text summarizer using EncoderDecoder NLP architecture with attention mechanism, achieving 97% accuracy.

Major Projects

2Projects

Smart Vibration and Ringtone Adjustment System

    Trained a CNN model on accelerometer data for real-time surface classification, achieving 98.8% accuracy and enabling automatic vibration/ringtone adjustment. Collected and processed sensor data, and optimized a TensorFlow model for on-device inference under 10MB ROM and 610KB RAM.

Samsung Cloud Emergency Backup

    Developed an on-device ML based emergency backup with TensorFlow Lite and unsupervised clustering, reducing backup latency by 50%.

Education

  • Bachelor of Technology in Computer Science and Engineering

    Guru Nanak Dev Engineering College (2022)