Responsibilities
Find out exactly what skills, experience, and qualifications you will need to succeed in this role before applying below.
- 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. xhqgsiq
- Experience working with enterprise data, security, permissions, and identity controls.
Generative AI Engineer in new york at Unknown Company
This position is listed as full time and onsite.