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GenAI Engineer – RAG Systems

4 weeks ago

India Gainwell Technologies Full time

About the Role

We are seeking a highly skilled and forward-thinking GenAI Engineer to join our AI innovation team. This role is ideal for someone with deep technical expertise in Generative AI, a strong foundation in Python programming, and a passion for driving enterprise AI transformation.

You will be instrumental in designing, developing, and deploying advanced Retrieval-Augmented Generation (RAG) systems. You'll also play a pivotal role in enabling our internal workforce to embrace and adopt AI technologies.

Key Responsibilities

  • Architect and implement scalable RAG systems using Python and modern GenAI tools.
  • Build custom pipelines for document ingestion, chunking strategies, and embedding generation. Working knowledge in LlamaIndex is preferable.
  • Have a deep knowledge in using AI augmented tools like GitHub Copilot. Experience in developing custom extensions
  • Evaluate and implement different embedding models (OpenAI, Azure OpenAI, Cohere, etc.) and chunking strategies (fixed-size, semantic-aware, overlap-based).
  • Create and optimize indexing strategies (vector, hybrid, keyword-based, hierarchical) for performance and accuracy.
  • Work with Azure AI Services, particularly Azure Cognitive Search and OpenAI integration, to deploy end-to-end AI applications.
  • Collaborate closely with cross-functional teams including data engineers, product managers, and domain experts.
  • Conduct AI enablement sessions, workshops, and hands-on labs to upskill internal teams on GenAI usage and best practices.
  • Participate in code reviews, contribute to best practices, and ensure the reliability, scalability, and maintainability of AI systems.

Required Qualifications

  • 5+ years of experience in software engineering, with strong expertise in Python.
  • 2+ years of proven track record of building and deploying RAG-based GenAI solutions.
  • Hands-on experience with LlamaIndex, LangChain, or equivalent frameworks.
  • Familiarity with prompt engineering, prompt tuning, and managing custom Copilot extensions.
  • Strong understanding of LLMs, vector databases (like FAISS, Pinecone, Azure Cognitive Search), and embedding techniques.
  • Solid knowledge of Azure AI, cloud deployment, and enterprise integration strategies.
  • Proficiency with version control and collaborative development using GitHub.

Preferred Qualifications

  • Experience with transformer models, fine-tuning, and inference optimization.
  • Working knowledge of MS Azure.
  • Contributions to open-source GenAI projects or technical blogs.
  • Previous experience in training or mentoring teams on AI adoption.