AI Development Specialist

4 days ago

India Steps AI Full time
Job Description

We are seeking a highly skilled and motivated Machine Learning Intern to join our dynamic team at Steps AI. As a leading AI company, we are focused on delivering innovative AI-powered products that revolutionize enterprise data retrieval and automation.

This role will place you at the forefront of cutting-edge AI technologies, including Large Language Models (LLMs), multi-agent systems, Retrieval-Augmented Generation (RAG), LLM Ops, Local LLM deployment/orchestration, and Generative AI (GenAI) strategies.

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:
  • A 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.
  • Mastery of designing, deploying, and maintaining production-level RAG pipelines with optimized retrieval, ranking, and generative model interactions.
  • 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 LangChain, Hugging Face, transformers, LlamaIndex, Ollama, or other model orchestration libraries.

We offer a flexible and dynamic work environment with opportunities for growth and development. If you are passionate about AI and want to be part of a team that is transforming how enterprises access and use data, please submit your application.

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