Gen AI Engineer

Job Details
Generative AI Engineer Agent Development Specialist
Remote
Long Term Contract

Overview
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.

Key Responsibilities
Design, build, and maintain autonomous or semi-autonomous AI agents using frameworks such as
Lang Graph, Autogen, Crew AI, or Bedrock (Lang graph 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 Cosmos DB 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 Gen AI 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.

Required Qualifications
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 Lang Graph, but experience with Autogen, Crew AI, 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 Cosmos DB, and designing scalable schemas for AI-driven systems.
Proficiency with Git/GitHub, including use of Gitlow or similar collaborative workflows.
Demonstrated ability to stay current on the latest GenAI models, protocols (e.g., Open AI Assistants, Function Calling, Lang Chain Agents), and research trends.

Preferred Qualifications
Experience deploying agents in cloud environments (Azure, AWS, or Google Cloud Platform).
Familiarity with model fine-tuning, embeddings generation, and Open AI 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.

Python
Prompt engineering
Understanding of AI agents
Understanding of vector databases
Understanding of current events in the GenAI field (most up to date models, ideally also awareness of how to use non-OpenAI models like Gemini and Claude)
Understanding of LangGraph (Ideally Autogen)
Understanding of CosmosDB and NoSQL
Bonus: AngularJS and Typescript (just for some specific use cases we're looking into right now, but really not required)