Lead AI Engineer
Core
Architect and deliver production-grade GenAI systems integrating LLMs, multi-agent workflows, and retrieval solutions.
Role type
Lead AI Engineer (GenAI/Agentic Systems)
Builds
Scalable intelligent applications, autonomous agent workflows, and reusable AI engineering assets.
Domain
Enterprise AI, Large Language Models, Multi-Agent Systems
Deliverable
production ML models | product features
Required skills
GenAI system architecture, LLM API integration, agent orchestration, RAG/vector database design, cloud-native AI deployment, observability, technical leadership, SDLC ownership
Preferred skills
Agentic AI workflow design, enterprise AI platform implementation, AI governance and cost management, technical mentoring
Technologies
Azure OpenAI, Azure AI Studio, Semantic Kernel, LangChain, AutoGen, Azure AI Search, Pinecone, Weaviate, FAISS, Azure Functions, Azure Container Apps, FastAPI, Docker, Azure DevOps, GitHub, Application Insights, OpenTelemetry, Azure Monitor, Datadog, New Relic, Model Context Protocol (MCP)
Responsibilities
Architect enterprise-grade GenAI systems, design autonomous agent workflows, optimize performance and latency, lead solution reviews and enforce safety protocols, collaborate on reusable patterns, guide engineering pods on design principles, build reusability assets like SDKs and templates (via careerplan.io/jobs/R00291249-lead-ai-engineer-at-ecolab)
Seniority
Senior, hands-on IC with leadership responsibilities
