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 :

Agent 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 production.

Production 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