Machine Learning

4 weeks ago

India Steps AI Full time

We are seeking a highly skilled and motivated Machine Learning Intern to join our team at Steps AI. As a key member of our dynamic team, you will contribute to advanced machine learning (ML), deep learning (DL), and natural language processing (NLP) projects.

The ideal candidate will have a deep understanding of transformer architectures, strong foundation of LLM techniques and models, strong MLOps foundations, and experience in deploying AI solutions in real-world applications.

Key Responsibilities:
  • LLM Development: Build multi-agent systems for enterprise use cases.
  • LLM Ops: Deploy scalable MLOps pipelines and optimize LLM performance in production.
  • Transformer Models: Work with advanced models (LLaMA, GPT, etc.), optimizing for cloud deployment.
  • RAG Pipelines: Design and implement efficient RAG systems (Graph, Struct, Naive) for enhanced retrieval.
  • Prompt Engineering: Refine prompts using techniques like CoT, ToT, and GoT to improve LLM accuracy.
  • Multi-Agent Systems: Develop LLM-based agents for dynamic tasks using orchestration frameworks.
  • Research: Prototype and Develop Production-level pipelines for advanced LLM orchestration and RAG.
Technical Skills Expectations:
  • Strong understanding of Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) with hands-on experience in Large Language Models (LLMs).
  • In-depth knowledge of transformer architectures, including BERT, GPT, T5, and state-of-the-art models such as GPT-4, Mistral, or LLaMA.
  • Proficiency in building and deploying RAG pipelines, with practical knowledge of Graph RAG, Agentic RAG, and Naive RAG methodologies.
  • Must have the ability to design, deploy, and maintain production-level RAG pipelines with optimized retrieval, ranking, and generative model interactions.
  • Pseudo-code comprehension and understanding of RAG systems will be evaluated during the interview process.
  • Experience in designing, deploying, and orchestrating multi-agent systems using LLM-based agents for dynamic task execution across various tools and platforms using langgraph, langchain, autogen, or llamaindex.
  • Familiarity with tool-calling frameworks, LLM-sandboxing, and how LLMs can interact with external systems (e.g., databases, APIs).
  • Strong foundation in MLOps practices for deploying scalable, production-ready AI models, with experience in Docker, Kubernetes, AWS, or Azure.
  • Experience with advanced prompting techniques, including Chain of Thought (CoT), Tree of Thought (ToT), Graph of Thought (GoT), to improve model reasoning and decision-making capabilities.
  • Proficiency with frameworks like PyTorch, TensorFlow, and frameworks like LangChain, Hugging Face, transformers, llamaindex, ollama, LLM360, texgen-webUI, or other model orchestration libraries
  • Excellent communication skills and the ability to work collaboratively with other AI engineers, DevOps professionals, and full-stack developers.
  • Ability to translate complex ML and AI concepts into actionable product features and deliverables for enterprise clients.
What We Offer:
  • Be one of the first to get hands-on experience on latest technologies, an opportunity to work with cutting-edge LLM and GenAI technologies, multi-agent systems, and real-world enterprise AI products.
  • Mentorship from experienced AI engineers and exposure to the latest tools, techniques, and research in AI.
  • A flexible and dynamic work environment with potential for future full-time positions based on performance.

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