LLM Application Developer

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

Skills Required

Sql, Nosql, Docker, FastAPI, Restful Apis, Python