We are looking for an experienced Generative AI Engineer skilled in agent-based system development. This position focuses on designing, implementing, and optimizing intelligent multi-agent workflows using advanced AI models and architectures. The ideal candidate is proficient in Python, AI agents, vector databases, and multi-agent frameworks, and is eager to advance autonomous AI agents in production settings.

Details:

• Role: Generative AI Engineer– Agent Development Specialist
• Location:  Remote
• US Hours overlap needed: 10am -6pm CET. possibility of a wider overlap (flexibility) appreciated
• Start: asap
• Duration: 6 months+ extension
• HackerRank challenge: YES

Scope:

• Design, build, and maintain autonomous or semi-autonomous AI agents using frameworks such as LangGraph, Autogen, CrewAI, or Bedrock (Langgraph preferred)
• Engineer sophisticated prompting strategies to drive consistent, effective agent performance across dynamic use cases.
• Architect 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).
• Leverage CosmosDB and other NoSQL data stores to manage large-scale, unstructured, and semi-structured data efficiently.
• Collaborate cross-functionally to integrate agent systems into broader products, APIs, and workflows.
• Continuously monitor the evolving GenAI landscape, evaluating new models, tools, protocols, and design patterns.
• Participate in code reviews, maintain code quality standards, and follow Git/GitHub workflows including branching, pull requests, and CI/CD practices.
• Conduct performance tuning and safety evaluations of AI agents across a variety of operational environments.

Requirements:

• 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 (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, 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 (e.g., OpenAI Assistants, Function Calling, LangChain Agents), and research trend.

Preferred Requirements:

• 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 (e.g., human-in-the-loop, automated feedback loops).
• Plus - Contributions to open-source AI projects or publications in the field.