Senior AI Engineer

Overview

As a Senior Data Scientist, you will lead the development of scalable GenAI-powered systems, designing intelligent workflows that leverage large language models (LLMs), vector-based retrieval, and multi-agent orchestration frameworks. You’ll drive solution architecture, mentor junior engineers, and deliver production-ready applications that integrate deeply with business processes and platforms.

Key Responsibilities

• Lead the design and deployment of GenAI systems leveraging LLMs, retrieval pipelines, and orchestration frameworks for multi-step task execution
• Architect and optimize prompt workflows, including chaining, templating, and context control, for high-accuracy and cost-efficient solutions
• Build and maintain embedding-based retrieval systems using vector databases and context-aware generation techniques (e.g., retrieval-augmented generation)
• Collaborate with product owners and engineering leads to align solution architecture with business objectives
• Guide and mentor junior engineers on best practices in prompt design, token optimization, security controls, and observability patterns
• Define standards for code modularity, response consistency, prompt safety, and testing across LLM-powered applications
• Maintain strong CI/CD practices using version-controlled workflows and cloud-native deployment pipelines
• Evaluate emerging GenAI tooling and provide technical recommendations for experimentation and adoption
  
  

• 4+ years of experience in AI/ML solution delivery, with a strong focus on GenAI or LLM-integrated systems
• Expertise in Python (v3.11+) with deep familiarity in LLM APIs, embedding generation, vector-based search, and modular pipeline design
• Proven experience in building and deploying prompt-driven applications at scale
• Solid understanding of agent orchestration patterns, multi-agent task flows, and context layering techniques
• Hands-on experience in cloud-native delivery (preferably Azure), including containerization, CI/CD, and monitoring
  
  

• Exposure to model context protocols (e.g., MCP) and agent-to-agent (A2A) coordination concepts
• Experience with LLM observability tools (latency tracking, relevance scoring, cost management)
• Contributor to internal or open-source projects that showcase applied GenAI, workflow orchestration, or prompt libraries
• Understanding of responsible AI guidelines, token-level safety, and enterprise security standards in GenAI applications
  
  

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.