AI/ML Intern

Role Overview

We are seeking a talented and enthusiastic AI/ML Developer with a strong focus on Generative AI to join our team in Mumbai. The ideal candidate should have good understanding or hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and embedding models. A solid proficiency in Python is essential, along with a strong foundation in coding, problem-solving, and understanding of the Software Development Life Cycle (SDLC).

Responsibilities

• Design, develop, and deploy Python applications
• Implement and optimize Retrieval-Augmented Generation (RAG) systems to enhance model performance and relevance.
• Work with various embedding models for natural language processing tasks.
• Develop and maintain robust, scalable, and efficient AI/ML solutions using Python, demonstrating strong coding and problem-solving abilities.
• Collaborate with cross-functional teams, applying good knowledge of SDLC principles to integrate AI solutions into products and services.
• Participate in the entire ML lifecycle, from data preprocessing to model deployment and monitoring.
• Stay up-to-date with the latest advancements in Generative AI, LLMs, and related technologies, including understanding parallel processing concepts.

Requirements

• 0-1 years of professional experience in AI/ML development.
• Demonstrated good knowledge of Large Language Models (LLMs) and their response mechanisms.
• Knowledge or experience with Retrieval-Augmented Generation (RAG) frameworks and embedding models.
• Very strong proficiency in Python programming and frameworks, with a proven track record in robust coding and problem-solving.
• Demonstrated understanding of Software Development Life Cycle (SDLC) principles.
• Understanding of parallel processing concepts.
• Proven knowledge or experience with FastAPI frameworks for building robust APIs.
• Ability to work effectively in an onsite team environment.

Skills

• Python
• FastAPI
• LLMs
• RAG
• MLOps

Nice to Have

• Knowledge and practical experience with MLOps principles and tools (e.g., Docker, Kubernetes, MLflow).
• Experience with cloud platforms (AWS, Azure, GCP) for deploying AI/ML models.
• Familiarity with deep learning frameworks such as TensorFlow or PyTorch.