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Generative AI Developer - RAG Architecture

Generative AI Developer - RAG Architecture

TESTQ Technologies LimitedDelhi, IN
9 days ago
Job type
  • Remote
Job description

Description :

We are seeking a highly specialized and experienced Generative AI Developer to join our team on a contract basis.

This is a mission-critical role focused on designing, building, and deploying advanced, production-ready AI solutions centered around Large Language Models (LLMs) and autonomous agents.

The ideal candidate will have deep expertise in Python, LangChain / LangGraph, fine-tuning, and robust RAG (Retrieval-Augmented Generation) pipeline development.

Key Responsibilities & Technical Deliverables and Application Development :

  • AI Agent Design : Design and build sophisticated AI agents and multi-agent systems using advanced frameworks like Python, LangChain, and LangGraph.
  • Prompt Engineering : Develop, test, and optimize complex prompt templates and structured reasoning workflows to enhance model accuracy, consistency, and task performance.
  • LLM Systems : Take ownership of the technical implementation and architecture for multi-agent or LLM-based systems, ensuring business logic is executed reliably.

Model Optimization and Data Integration :

  • RAG Architecture : Build and optimize Retrieval-Augmented Generation (RAG) pipelines, including the efficient integration of internal knowledge bases with relevant vector databases (e.g., Pinecone, Chroma).
  • Model Training : Implement fine-tuning and training protocols for both proprietary and open-source language models to achieve superior performance on specific, high-value enterprise tasks.
  • Model Optimization : Apply techniques for model quantization, pruning, and efficiency to reduce inference costs and latency in Readiness & MLOps :
  • Deployment : Prepare and structure AI solutions for production deployment and large-scale scalability, utilizing best practices for cloud environment setup.
  • Evaluation & Observability : Develop and implement rigorous AI observability and evaluation frameworks to continuously monitor model drift, hallucination rates, and overall system performance in a live environment.
  • DevOps Best Practices : Apply familiarity with Docker and deployment best practices for AI applications to containerize and manage microservices efficiently.
  • Required Skills & Technical Expertise :

  • Core Foundation (Mandatory) : Strong Python and AI / ML background with demonstrable experience applying machine learning concepts to real-world problems.
  • LLM Systems (Mandatory) : Extensive, hands-on experience building and deploying multi-agent or complex LLM-based systems using modern orchestration frameworks (LangChain / LangGraph).
  • Advanced Techniques (Mandatory) : Proven proficiency in fine-tuning, RAG pipeline construction, and model optimization techniques.
  • Production Readiness : Strong grasp of software engineering principles, version control (Git), and deployment best practices for scalable cloud-based AI applications.
  • Tooling (Strong Asset) : Experience with Docker, vector databases, and relevant MLOps tools
  • (ref : hirist.tech)

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