> Markdown version of [/jobs/ext/4386571-generative-ai-engineer](https://www.wearedevelopers.com/jobs/ext/4386571-generative-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Generative AI Engineer

- **Company:** THE PHOENIX
- **Location:** Mundelein, IL, United States
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Software as a Service, Databases, Python (Programming Language), Open Source Technology, OpenAI, Search Technologies, Systems Integration, Enterprise Data Management, Pinecone, Qdrant, Enterprise Software Applications, Chatbots, LangChain, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Generative AI, Agentic-AI, Build Management, Pgvector, CrewAI, Weaviate, AutoGen, Build Tools, Milvus, Machine Learning Operations, Model Context Protocol, Semantic Kernel
- **Published:** October 27, 2026
- **Apply:** https://www.thejobnetwork.com/job/8c2ba5b1-dfef-438f-9852-aa892ce1e1b7/generative-ai-engineer

## About the Role

n
\n

 * Strong Python development experience and software engineering fundamentals.
\n

 * Hands-on experience developing Generative AI or LLM-powered applications.
\n

 * Experience building AI agents or agentic workflows, beyond standalone chatbots or prompt-based applications.
\n

 * Experience with LLM APIs, function/tool calling, structured outputs, and model orchestration.
\n

 * Understanding of RAG, embeddings, vector databases, semantic search, and context management.
\n

 * Experience integrating AI applications with APIs, databases, SaaS platforms, or enterprise systems.
\n

 * Familiarity with agent frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or comparable technologies.
\n

 * Experience deploying AI applications in Azure, AWS, or GCP.
\n

 * Understanding of AI evaluation, observability, security, and guardrails.
\n, * Experience designing multi-agent systems.
\n

 * Experience with Model Context Protocol (MCP).
\n

 * Experience building agents that automate end-to-end business processes.
\n

 * Knowledge of LLMOps/MLOps and production monitoring.
\n

 * Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.
\n

 * Experience working with enterprise data, security, permissions, and identity controls.
\n

## Description

n
\n

 * Design and build agentic AI applications that can reason, plan, execute tasks, and interact with external tools and systems.
\n

 * Develop single-agent and multi-agent architectures for complex business workflows.
\n

 * Build tool-calling capabilities that allow agents to interact with APIs, databases, enterprise applications, and internal services.
\n

 * Develop and optimize RAG pipelines, vector search, embeddings, and contextual retrieval.
\n

 * Integrate LLMs from platforms such as OpenAI, Anthropic, Google, and open-source providers.
\n

 * Build orchestration workflows using LangGraph, LangChain, Semantic Kernel, AutoGen, or similar technologies.
\n

 * Implement memory, state management, guardrails, human-in-the-loop controls, and structured outputs.
\n

 * Develop evaluation frameworks to measure agent accuracy, reliability, latency, and task completion.
\n

 * Deploy, monitor, and improve AI applications in production environments.
\n

 * Partner with engineering, data, security, product, and business teams to identify opportunities for agent-driven automation.
\n