Job Title:  Lead Senior Associate | Engineering Foundry & Managed Services | Bengaluru | Engineering as a Servic

Principal AI Engineer 

As a Principal AI Engineer, you will lead the design and evolution of Ecolab’s GenAI and agentic AI systems at a strategic level. You will set architectural direction, establish AI governance protocols (including security, context control, and responsible use), and influence how Ecolab integrates advanced AI across its digital ecosystem. This role is ideal for deep technical experts who also bring system thinking, hands-on delivery experience, and strong cross-functional influence. 

Core Responsibilities 

• Define enterprise-wide architecture for GenAI systems, agent frameworks, LLM integration, and multi-agent orchestration 
• Establish AI governance standards — including security, ethical usage, context management (MCP), agent-to-agent protocols (A2A), and explainability 
• Lead evaluation and integration of emerging AI tooling and frameworks, from retrieval infrastructure to observability platforms 
• Serve as a cross-functional advisor on LLM implementation across product teams, engineering pods, and data science 
• Develop reusable components, SDKs, or templates that standardize GenAI deployment patterns 
• Review and challenge architectural proposals from Lead Engineers and ensure alignment with long-term AI strategy 
• Build and drive internal capability around GenAI innovation, including mentoring, tech talks, and hackathons 
• Support cross-region scalability, data sensitivity considerations, and deployment performance benchmarking 
• Stay abreast of industry trends and translate them into actionable design or investment recommendations 

Additional Software Engineering Responsibilities: 

• Drive implementation of CI/CD pipelines, automated testing, and deployment strategies for GenAI workloads 
• Ensure robust observability through logging, monitoring, and tracing of AI services in production 
• Architect distributed systems and microservices to support scalable AI applications 
• Collaborate with DevOps and platform engineering teams to optimize containerization (Docker/Kubernetes) and cloud-native deployments 
• Establish coding standards, design patterns, and best practices for AI/ML engineering teams 

Required Skills 

• 8+ years of experience in AI/ML system design, with solid implementation in GenAI, LLMs, or agentic architectures 
• Proven experience in architecting production-grade GenAI platforms or frameworks at scale 
• Deep proficiency in Python, with mastery over GenAI libraries: OpenAI, transformers, Pydantic, LangChain, FAISS, Pinecone, etc. 
• Demonstrated experience integrating AI into cloud environments (Azure preferred: Databricks, Azure Foundry) 
• Strong knowledge of AI security, privacy frameworks, hallucination mitigation, and governance 

Additional Software Engineering Skills: 

• Expertise in distributed systems, API design, and microservices architecture 
• Strong background in software engineering fundamentals: data structures, algorithms, and design patterns 
• Hands-on experience with CI/CD pipelines, GitOps, and automated testing frameworks 
• Proficiency in containerization and orchestration (Docker, Kubernetes) for AI workloads 
• Familiarity with observability tools (Prometheus, Grafana, ELK stack) for monitoring AI systems 
• Experience with performance optimization, benchmarking, and scaling AI services in production 

Nice-to-Have Skills 

• Hands-on implementation of MCP, A2A orchestration, or autonomous agent workflows 
• Contributor to open-source or community-based GenAI projects 
• Experience driving AI strategy across a multi-product or multi-cloud ecosystem 
• Strong executive presence with the ability to translate deep tech to business impact 
• Published thought leadership (papers, blogs, talks) in the AI/LLM space 

Preferred Qualifications 

• Advanced degree (MS/PhD) in Computer Science, Software Engineering, or related field with specialization in AI/ML 
• Experience leading large-scale engineering teams in building AI-driven platforms and services 
• Strong expertise in cloud-native architectures, serverless computing, and hybrid/multi-cloud deployments 
• Track record of delivering enterprise-grade AI solutions with high availability, fault tolerance, and disaster recovery planning 
• Contributions to open-source AI/ML frameworks or standards bodies 
• Demonstrated ability to align AI engineering practices with business strategy and measurable outcomes 
• Experience with compliance frameworks (GDPR, HIPAA, SOC2) in AI system design 
• Ability to mentor senior engineers and influence executive stakeholders on AI adoption strategies