The ideal candidate will have a minimum of 2+ years of relevant experience, a good understanding of LLMs, LangGraph, LangChain, and AutoGen is a professional who designs, develops, and deploys intelligent systems utilizing large language models (LLMs) and advanced AI frameworks.

Python Proficiency :

- Strong programming skills in Python are fundamental for developing and implementing AI solutions.

Prompt Engineering :

- Expertise in crafting effective prompts to guide LLMs towards generating desired and accurate responses, often involving techniques like prompt chaining and optimization.

LLM Application Development :

- Hands-on experience in building applications powered by various LLMs (e.g., GPT, LLaMA, Mistral).

- This includes understanding LLM architecture, memory management, and function/tool calling.

Agentic AI Frameworks :

Proficiency with frameworks designed for building AI agents and multi-agent systems, such as :

- LangChain : A framework for developing applications powered by language models, enabling chaining of components and integration with various tools and data sources.

- LangGraph : An extension of LangChain specifically designed for building stateful, multi-actor applications using LLMs, often visualized as a graph of interconnected nodes representing agents or logical steps.

- AutoGen : A Microsoft framework that facilitates multi-agent collaboration, allowing specialized agents to work together to solve complex problems through task decomposition and recursive feedback loops.

Retrieval-Augmented Generation (RAG) :

- Experience in implementing and optimizing RAG pipelines, which combine LLMs with external knowledge bases (e.g., vector databases) to enhance generation with retrieved information.

Deployment and MLOps :

- Practical knowledge of deploying AI models and agents into production environments, including containerization (Docker), orchestration (Kubernetes), cloud platforms (AWS, Azure, GCP), and CI/CD pipelines.