GenAI Engineer (40242)

I'm looking for a Generative AI Engineer. This role involves developing autonomous or semi-autonomous agents using LangGraph or similar frameworks, engineering prompt strategies for LLMs, and architecting RAG pipelines using tools like FAISS, Azure AI Search, or Pinecone. You’ll need solid Python skills, experience with NoSQL databases like CosmosDB, and a deep understanding of multi-agent orchestration. Your solutions will be embedded into live products, APIs, and data pipelines, with a focus on reliability, safety, and scale. Bonus if you’ve worked in cloud environments or contributed to open-source AI tools. If you’re ready to build GenAI that delivers—let’s talk.

🚀 Project
- designing, building, and maintaining autonomous or semi-autonomous AI agents using frameworks such as LangGraph, Autogen, CrewAI, or Bedrock
- engineering sophisticated prompting strategies to drive consistent, effective agent performance across dynamic use cases
- architecting end-to-end solutions that integrate vector databases (e.g., Azure AI Search, FAISS, Pinecone) with real-time or batch ETL pipelines to power agent memory and retrieval-augmented generation (RAG)
- leveraging CosmosDB and other NoSQL data stores to manage large-scale, unstructured, and semi-structured data efficiently
- collaborating cross-functionally to integrate agent systems into broader products, APIs, and workflows
- continuously monitoring the evolving GenAI landscape, evaluating new models, tools, protocols, and design patterns
- participating in code reviews, maintaining code quality standards, and following Git/GitHub workflows including branching, pull requests, and CI/CD practices
- conducting performance tuning and safety evaluations of AI agents across a variety of operational environments

🎯 Skills
- strong programming skills in Python, including OOP principles and production-level code design
- demonstrated experience with prompt engineering techniques for large language models (LLMs) like GPT models, Claude, Gemini, or open-source equivalents
- deep understanding of AI agent concepts including memory management, planning, tool use, autonomous task execution, and evaluation metrics
- working knowledge of multi-agent orchestration frameworks, preferably LangGraph, but experience with Autogen, CrewAI, or similar is also valuable
- experience with vector databases (Azure AI Search, Pinecone, FAISS, Chroma) for embedding storage and semantic search
- understanding of ETL processes and data transformation pipelines in both batch and streaming architectures
- familiarity with NoSQL databases, specifically CosmosDB, and designing scalable schemas for AI-driven systems
- proficiency with Git/GitHub, including use of Gitflow or similar collaborative workflows
- demonstrated ability to stay current on the latest GenAI models, protocols (OpenAI Assistants, Function Calling, LangChain Agents), and research trend

💡 Nice to have
- experience deploying agents in cloud environments (Azure, AWS, or GCP)
- familiarity with model fine-tuning, embeddings generation, and OpenAI plugin/tool calling
- exposure to observability and evaluation techniques for AI systems (human-in-the-loop, automated feedback loops)
- contributions to open-source AI projects or publications in the field