AI / ML Intern (Beginner Level)

Desirous Global Consulting; Hyderabad; Hybrid; INR 180,000 - 300,000; Full time

Job summary

Desirous Global Consulting Pvt Ltd. invites applications for an AI / ML Intern (Beginner Level) in Hyderabad. The role targets students or fresh graduates who want hands-on exposure to ML fundamentals, NLP, GenAI workflows, and model deployment concepts.

Prior industry experience is not required, and the internship is designed for eager learners. The position offers a 3-6 month duration with a flexible schedule, remote/onsite/hybrid options, and a stipend in the local currency.

Qualifications

• Proficiency in Python for data handling and ML experimentation.
• Solid understanding of ML fundamentals: training, validation, metrics, overfitting.
• Hands-on experience with at least one ML/DL framework (Scikit-learn / TensorFlow / PyTorch).
• Familiar with Pandas, NumPy, and Jupyter notebooks.
• Basic knowledge of APIs (REST) and how models are consumed in applications.
• Strong problem-solving mindset and willingness to learn.

Responsibilities

• Assist in data cleaning, preprocessing and EDA.
• Support model training and evaluation using ML frameworks.
• Work on basic NLP tasks using HuggingFace and prompt engineering.
• Contribute to GenAI workflows using LLM APIs and LangChain.
• Contribute to vector search/retrieval (FAISS / Pinecone / Weaviate / MongoDB Vector Search).
• Develop lightweight APIs for model inference using FastAPI / Flask.
• Document experiments, results and learnings in Jupyter notebooks.

Skills

Python

ML fundamentals

ML frameworks

Pandas/NumPy/Jupyter

REST APIs

problem solving

Education

B.E./B.Tech/MCA/M.Tech/BCA/B.Sc (CS/IT) or related

Job description

AI / ML Intern (Beginner Level)

Medical Services/Hospital Full-Time Job ID: DGC01257

Hyderabad, Telangana 0-1 Yrs ₹1.8 - ₹300000 Yearly

Location: Remote / Onsite / Hybrid

Duration: 3-6 Months

Internship Type: Full-time / Part-time

Job Overview

We are looking for an AI / ML Intern who is eager to learn how machine learning models are built, trained, evaluated, and deployed in real-world products. This role is ideal for students or fresh graduates who want hands-on exposure to ML fundamentals, deep learning, NLP, and GenAI workflows. No prior industry experience is required - we value curiosity, consistency, and a strong willingness to learn.

Key Responsibilities

• Assist in data cleaning, preprocessing, and exploratory data analysis (EDA).
• Support model training and evaluation using Scikit-learn / TensorFlow / PyTorch.
• Work on basic NLP tasks using HuggingFace Transformers and prompt engineering.
• Help build simple GenAI workflows using LLM APIs and LangChain.
• Contribute to vector search / retrieval (FAISS / Pinecone / Weaviate / MongoDB Vector Search).
• Develop lightweight APIs for model inference using FastAPI / Flask.
• Document experiments, results, and learnings in Jupyter notebooks.

Required Skills (Must Have)

• Basic proficiency in Python for data handling and ML experimentation.
• Understanding of ML fundamentals (training, validation, metrics, overfitting).
• Hands-on familiarity with at least one ML/DL framework (Scikit-learn / TensorFlow / PyTorch).
• Comfortable with Pandas, NumPy, and Jupyter notebooks.
• Basic knowledge of APIs (REST) and how models are consumed in applications.
• Strong problem-solving mindset and willingness to learn.

Preferred Skills (Good to Have)

• Exposure to NLP or GenAI tools (HuggingFace, LLM APIs, LangChain).
• Understanding of vector databases / semantic search (FAISS, Pinecone, Weaviate, MongoDB Vector Search).
• Basic experience with FastAPI / Flask or Node.js for integration.
• Familiarity with Git, Docker, and simple MLOps concepts (MLflow, DVC).
• Any cloud exposure (AWS / SageMaker is a plus).

Qualifications

• Pursuing or recently completed B.E / B.Tech / MCA / M.Tech / BCA / B.Sc (CS/IT) or related field.
• Final-year students and freshers are welcome.
• Ability to commit consistently for the internship duration.

What You Will Learn

• Deep learning basics and practical experimentation with TensorFlow / PyTorch.
• NLP & GenAI workflows using Transformers, prompt engineering, and LLM APIs.
• Retrieval-Augmented Generation (RAG) fundamentals with vector search.
• Serving models with FastAPI and integrating AI into applications.
• Experiment tracking and reproducibility using MLflow / DVC and Docker.