As a Lead AI Engineer, you will own the architecture and delivery of GenAI-based systems that integrate large language models (LLMs), multi-agent workflows, and embedding-powered retrieval solutions. You will guide cross-functional pods, define engineering standards, and drive innovation through scalable, production-grade intelligent applications. You will lead a team of associates both functionally and admin responsibilities.

• Architect enterprise-grade GenAI systems using modular LLM APIs, agent orchestration frameworks, and embedding pipelines
• Design and implement autonomous agent workflows with context management, multi-agent coordination, and task delegation
• Optimize performance, latency, and accuracy through experimentation with prompt strategies, retrieval layers, and caching logic
• Lead solution reviews, enforce prompt safety and governance, and ensure alignment with security protocols
• Collaborate with platform, product, and engineering leads to define reusable patterns and scalable AI capabilities
• Guide engineering pods on GenAI design principles, system reliability, and prompt lifecycle management
• Build and maintain reusability assets — SDKs, templates, shared agent logic — to accelerate delivery velocity across teams
• Stay up to date with advancements in LLM tooling, orchestration abstractions, and prompt optimization techniques

• 6 to 8+ years of experience in software, AI, or ML engineering roles, including significant experience designing, delivering, and operating production-grade GenAI or agentic AI applications
• Proven experience leading the technical delivery of LLM-powered products or agent-based solutions, including solution design, engineering guidance, and operational readiness
• Strong technical foundation in Python and modern backend engineering patterns, with practical experience building AI-enabled application services and APIs
• Hands-on experience with Azure OpenAI, Azure AI Studio, Semantic Kernel, LangChain, AutoGen, or equivalent platforms and orchestration frameworks, including real-world use of LLM APIs, prompt workflows, tool calling, and agent coordination
• Strong experience designing and implementing retrieval-augmented generation (RAG) and vector-based patterns using platforms such as Azure AI Search, Pinecone, Weaviate, FAISS, or equivalent
• Experience building and deploying cloud-native AI services using technologies such as Azure Functions, Azure Container Apps, FastAPI, Docker, Azure DevOps, GitHub, or equivalent engineering and deployment platforms
• Solid understanding of CI/CD, containerization, automated testing, and production deployment practices for AI-driven systems
• Practical experience with observability and operational tooling such as Application Insights, OpenTelemetry, Azure Monitor, Datadog, New Relic, or equivalent, including monitoring of reliability, latency, and cost
• Exposure to Model Context Protocol (MCP), agent-to-agent (A2A) interaction patterns, or similar context-sharing and distributed agent communication approaches
• Strong ownership mindset across the full SDLC, including design, build, deployment, support, reliability improvement, and long-term maintainability
• Proven ability to raise engineering quality through code reviews, technical mentoring, design guidance, and reuse of shared patterns and components
• Strong collaboration and communication skills, with the ability to work effectively across engineering, architecture, product, and platform teams

• Experience leading the design or implementation of agentic AI workflows involving multi-step reasoning, tool orchestration, and reusable orchestration patterns
• Experience with Microsoft AI Foundry, Azure Machine Learning, Azure AI / Copilot Studio, or equivalent platforms used for enterprise AI solution development and experimentation
• Familiarity with enterprise integration and application ecosystems, including AI integration with APIs, workflow platforms, and downstream business systems
• Experience contributing to reusable GenAI accelerators, prompt libraries, orchestration templates, internal AI developer platforms, or engineering toolkits
• Familiarity with AI governance, safety, observability, and cost-management tooling, including token usage analytics, quality evaluation, and guardrail implementation
• Experience supporting technical direction for other engineers through architecture reviews, implementation guidance, and technical mentoring
• Ability to communicate complex technical decisions clearly to both engineers and non-technical stakeholders
• Experience operating in a build-own-operate product environment with strong expectations around reliability, supportability, and continuous improvement

Our Commitment to a Culture of Inclusion & Belonging
Ecolab is committed to fair and equal treatment of associates and applicants and furthering the principles of Equal Opportunity to Employment. We will recruit, hire, promote, transfer and provide opportunities for advancement based on individual qualifications and job performance in all matters affecting employment, compensation, benefits, working conditions, and opportunities for advancement. Ecolab will not discriminate against any associate or applicant for employment because of race, religion, color, creed, national origin,citizenship status, sex, sexual orientation, gender identity and expressions, genetic information, marital status, age, or disability.