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.