AI / ML Research Intern

Role Overview

AI / ML Research Intern

Key Responsibilities

Machine Learning & LLMs

• Develop and train models for

  supervised, unsupervised, and reinforcement learning

  tasks.
• Fine-tune

  Large Language Models (LLMs)

  such as GPT, Llama, Falcon, or Mistral on

  custom datasets

  .
• Build and optimize

  domain-specific chatbots

  and

  Retrieval-Augmented Generation (RAG)

  systems.
• Experiment with

  parameter-efficient fine-tuning (LoRA, PEFT)

  and

  embedding-based retrieval

  using

  vector databases (FAISS, Pinecone, Chroma)

  .
• Integrate and deploy LLMs using

  LangChain

  ,

  Hugging Face Transformers

  , and related frameworks.
• Contribute to

  agentic AI systems

  capable of autonomous reasoning and multi-step task execution.

Computer Vision & AI

• Design and train

  object detection, segmentation, and classification models

  using

  YOLO

  ,

  OpenCV

  , and

  PyTorch/TensorFlow

  .
• Handle

  dataset creation, annotation, and augmentation

  for vision-based projects.
• Develop

  real-time inference pipelines

  for image/video data and optimize them for edge or cloud deployment.
• Implement

  AI-based perception modules

  and integrate them with decision-making systems.
• Conduct

  research experiments

  on multimodal AI (vision + language integration).

Technical Skills Required

• Proficiency in

  Python

  and ML/DL frameworks:

  PyTorch, TensorFlow, Keras, Scikit-learn

  .
• Hands-on experience with

  Hugging Face Transformers

  ,

  LangChain

  , and

  OpenAI APIs

  .
• Strong knowledge of

  YOLO

  ,

  OpenCV

  , and

  deep learning architectures

  (CNNs, Transformers, Vision Transformers).
• Familiarity with

  annotation tools

  (LabelImg, CVAT) and

  data preprocessing techniques

  .
• Understanding of

  machine learning algorithms

  ,

  evaluation metrics

  , and

  optimization methods

  .
• Exposure to

  agentic AI frameworks

  ,

  multimodal learning

  , or

  AI workflow automation

  is a plus.

Educational Qualification

• M.Tech student

  specializing in

  Artificial Intelligence, Machine Learning, Computer Science, or related field.

Preferred Qualifications

• Research or thesis experience in

  LLMs, NLP, or Computer Vision

  .
• Familiarity with

  cloud training environments

  (AWS / GCP / Azure) and

  GPU optimization

  .
• Hands-on project experience with

  RAG pipelines

  ,

  chatbots

  , or

  visual analytics systems

  .
• Understanding of

  MLOps

  concepts (MLflow, Weights & Biases, or DVC).