GenAI Engineer (39014)

I'm looking for an GenAI Engineer to build next-gen autonomous agents using frameworks like LangGraph and integrate them with real-world systems. You'll design robust prompt strategies, work with vector databases, and optimize agents for performance and safety. If you're fluent in Python, understand agent architectures, and thrive at the cutting edge of GenAI, let’s talk. Experience with CosmosDB, CI/CD, and modern LLM stacks is essential. Bonus if you've deployed agents in cloud environments or contributed to open-source.

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

🎯 Skills
- strong programming skills in Python, including OOP principles and production-level code design
- demonstrated experience with prompt engineering techniques for LLMs (e.g., GPT models, Claude, Gemini, or open-source alternatives)
- deep understanding of AI agent concepts such as memory management, planning, tool use, autonomous task execution, and evaluation metrics
- working knowledge of multi-agent orchestration frameworks, preferably LangGraph, or alternatives like Autogen or CrewAI
- experience with vector databases (e.g., 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, particularly CosmosDB, and designing scalable schemas for AI-driven systems
- proficiency with Git/GitHub, including collaborative workflows such as Gitflow
- demonstrated ability to stay current with emerging GenAI models, protocols (e.g., OpenAI Assistants, Function Calling, LangChain Agents), and research trends

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