LLM Application Developer

5 days ago

Ahmedabad, Gujarat, India Codevally Full time ₹ 12,00,000 - ₹ 18,00,000 per year

Key Responsibilities

  • Agentic System Design:
    Design, develop, and deploy
    autonomous AI agents
    capable of complex, goal-oriented reasoning, planning, and task execution using modern agentic frameworks.
  • RAG System Development:
    Build and optimize robust
    Retrieval-Augmented Generation (RAG)
    pipelines to ground LLMs in proprietary data, ensuring factual accuracy and data security.
  • Tool Integration & Function Calling:
    Equip AI agents with the ability to use external APIs, databases, and custom tools to perform actions in the real world.
  • Orchestration Frameworks:
    Master and implement core logic using orchestration tools like
    LangChain, LlamaIndex, LangGraph, or CrewAI
    to manage conversation state and agent workflows.
  • Prompt Engineering & Alignment:
    Develop systematic Prompt Engineering strategies to maximize agent reliability and output quality.
  • Production Deployment:
    Develop secure, low-latency API services (using
    Python/FastAPI
    ) to serve LLM applications, ensuring high availability and scalability.
  • Evaluation & Quality Assurance (Evals):
    Design and implement continuous evaluation frameworks
    to measure performance against business metrics. This includes developing
    ground truth datasets
    , implementing
    LLM-as-a-Judge
    scoring, and monitoring for
    hallucinations, factual correctness, and prompt injection
    .

Required Skills and Qualifications

  • Programming:
    3+ years
    of professional software development experience, with expert proficiency in
    Python
    .
  • LLM Application Experience:
    Direct experience building and deploying production-level applications using major LLM APIs or open-source models.
  • Core RAG Expertise:
    Deep practical knowledge of designing, implementing, and optimizing a RAG system and proficiency with
    Vector Databases
    (e.g., Pinecone, Chroma, Milvus).
  • Agent Orchestration:
    Proven experience with at least one major agent orchestration library (
    LangChain, LlamaIndex, or similar state machine/graph-based frameworks
    ).
  • Evaluation Tools:
    Hands-on experience with LLM evaluation frameworks like
    RAGAS, DeepEval, or LangSmith
    to create automated performance benchmarks.
  • Back-end Engineering:
    Strong experience developing and maintaining RESTful APIs and integrating with various data sources (SQL/NoSQL).
  • DevOps Fundamentals:
    Working knowledge of
    Docker, CI/CD pipeline
    and cloud infrastructure for deployment.

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