How You Will Contribute

• Design and develop AI agents using frameworks like LangChain, LangGraph, Langflow, and MCP Servers.

• Fine-tune and optimize large language models (LLMs) such as GPT models, Llama, and others for diverse applications.

• Implement Retrieval-Augmented Generation (RAG) techniques and integrate vector databases like Qdrant and ChromaDB.

• Enhance AI agent operations with tools like langfuse and litellm, ensuring robust security and guardrails.

• Leverage cloud platforms such as AWS, Google Cloud, and Azure for scalable AI solution deployment.

• Build and manage databases using Postgres, Neo4j, and Clickhouse for efficient data handling.

• Utilize technologies like Apache Airflow, Redis, Mlflow, Minio, Apache Kedro, and PySpark for workflow optimization and data processing.

The Must Haves

• 5+ years of experience in AI engineering with proficiency in Python.

• Expertise in AI frameworks such as LangChain and LangGraph, or similar agentic AI frameworks.

• Proven experience in fine-tuning large language models (LLMs) and implementing Retrieval-Augmented Generation (RAG) techniques.

• Strong knowledge of vector databases and AI agent security protocols.

• Familiarity with cloud platforms and database technologies.

• Experience with prompt engineering techniques and CI/CD processes.

• Background in leveraging tools like Apache Airflow, Redis, Mlflow, Minio, and Apache Kedro.

Nice to Haves

• Experience with PySpark for large-scale data processing.

• Knowledge of emerging AI frameworks and technologies.

• Advanced understanding of AI agent optimization and collaborative systems.

• Exposure to cloud-native tools and services for AI deployment.

• Familiarity with advanced database architectures and tools.

• Interest in contributing to open-source AI initiatives.

• Passion for driving innovation in AI technologies.

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