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Machine Learning Hardware Engineer

Machine Learning Hardware Engineer

RGBSIMadurai, IN
20 hours ago
Job description

Most Important Skills / Responsibilities :

  • Lead RAG Architecture Design – Define and implement best practices for retrieval-augmented generation systems, ensuring reliability, scalability, and low-latency performance.
  • Full-Stack AI Development – Build and optimize multi-stage pipelines using LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, or custom).
  • Programming & Integration – Develop services and APIs in Python and Golang to support AI workflows, document ingestion, and retrieval processes.
  • Search & Retrieval Optimization – Implement hybrid search, vector embeddings, and semantic ranking strategies to improve contextual accuracy.
  • Prompt Engineering – Design and iterate on few-shot, chain-of-thought, and tool-augmented prompts for domain-specific applications.
  • Strong proficiency in Python and Golang or RUST, with experience building high-performance services and APIs.
  • Need experience in developing end-to-end projects ->

backend software(RAG / GenAI, text / image, model improvement, scoring) ->

AWS / databricks(deployment) ->

endpoint ->

chatbot

Key Responsibilities

  • Lead RAG Architecture Design – Define and implement best practices for retrieval-augmented generation systems, ensuring reliability, scalability, and low-latency performance.
  • Full-Stack AI Development – Build and optimize multi-stage pipelines using LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex, or custom).
  • Programming & Integration – Develop services and APIs in Python and Golang to support AI workflows, document ingestion, and retrieval processes.
  • Search & Retrieval Optimization – Implement hybrid search, vector embeddings, and semantic ranking strategies to improve contextual accuracy.
  • Prompt Engineering – Design and iterate on few-shot, chain-of-thought, and tool-augmented prompts for domain-specific applications.
  • Mentorship & Collaboration – Partner with cross-functional teams and guide engineers on RAG and LLM best practices.
  • Performance Monitoring – Establish KPIs and evaluation metrics for RAG pipeline quality and model performance.

    • Qualifications

    Must Have :

  • 8+ years in software engineering or applied AI / ML, with at least 2+ years focused on LLMs and retrieval systems.
  • Strong proficiency in Python and Golang or RUST, with experience building high-performance services and APIs.
  • Expertise in RAG frameworks (LangChain, LangGraph, LlamaIndex) and embedding models.
  • Hands-on experience with vector databases (Databricks Vector Store, Pinecone, Weaviate, Milvus, Chroma).
  • Strong understanding of hybrid search (semantic + keyword) and embedding optimization.
  • Bachelors degree required

    • Preferred :

  • LLM fine-tuning experience (LoRA, PEFT).
  • Knowledge graph integration with LLMs.
  • Familiarity with cloud ML deployment (AWS (preferred), Databricks, Azure).
  • Masters or PHD degree in CS

    • Soft Skills

  • Strong problem-solving and decision-making skills under tight timelines.
  • Excellent communication for cross-functional collaboration.
  • Ability to work independently while aligning with strategic goals.
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