Generative AI Engineer

Responsibilities Design and build agentic AI applications that can reason, plan, execute tasks, and interact with external tools and systems. Develop single-agent and multi-agent architectures for complex business workflows. Build tool-calling capabilities that allow agents to interact with APIs, databases, enterprise applications, and internal services. Develop and optimize RAG pipelines, vector search, embeddings, and contextual retrieval. Integrate LLMs from platforms such as OpenAI, Anthropic, Google, and open-source providers. Build orchestration workflows using LangGraph, LangChain, Semantic Kernel, AutoGen, or similar technologies. Implement memory, state management, guardrails, human-in-the-loop controls, and structured outputs. Develop evaluation frameworks to measure agent accuracy, reliability, latency, and task completion. Deploy, monitor, and improve AI applications in production environments. Partner with engineering, data, security, product, and business teams to identify opportunities for agent-driven automation. Qualifications Strong Python development experience and software engineering fundamentals. Hands-on experience developing Generative AI or LLM-powered applications. Experience building AI agents or agentic workflows, beyond standalone chatbots or prompt-based applications. Experience with LLM APIs, function/tool calling, structured outputs, and model orchestration. Understanding of RAG, embeddings, vector databases, semantic search, and context management. Experience integrating AI applications with APIs, databases, SaaS platforms, or enterprise systems. Familiarity with agent frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or comparable technologies. Experience deploying AI applications in Azure, AWS, or GCP. Understanding of AI evaluation, observability, security, and guardrails. Preferred Experience designing multi-agent systems. Experience with Model Context Protocol (MCP). Experience building agents that automate end-to-end business processes. Knowledge of LLMOps/MLOps and production monitoring. Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector. Experience working with enterprise data, security, permissions, and identity controls.