Ecolab Inc.
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Bengaluru · On-site · 12+ Yrs · 2 days ago
Chemical Manufacturing · 10000+ Employees · Saint Paul, US
A trusted partner for millions of customers, Ecolab (ECL) is a global sustainability leader offering water, hygiene and infection prevention solutions and services that help protect people and the resources vital to life. Building on a century of innovation, Ecolab has annual sales of $14 billion and more than 47,000 associates, Ecolab delivers comprehensive science-based solutions, data-driven insights and world-class service to advance food safety, help maintain clean and safe environments, optimize water and energy use, and improve operational efficiencies and sustainability for customers in the food, healthcare, hospitality and industrial markets in more than 170 countries around the world. When you come to work at Ecolab, you get to take on some of the world’s most meaningful challenges and have the opportunity to learn and grow, shape your career, make an impact and quickly see the importance of your work. For more Ecolab news and information, visit www.ecolab.com. Follow us on Twitter @ecolab, Facebook at facebook.com/ecolab, LinkedIn at Ecolab or Instagram at Ecolab Inc. To explore career opportunities at Ecolab, visit jobs.ecolab.com.
Hiring: Engineering
What employees say
+ - Best Organization for learning, as there are multiple working areas within the organization an individual can work on. - Customers experience, ecolab works on B2B products and d
− - Limited opportunities to work on. - Environment of the work only depends on the manage/team leader. - Average salaries
More reviews on the company profile.
Glassdoor median (AI Engineer): 19.8 LPA
Interview experience · AI Engineer
Round 1: Technical round with easy DSA with Sr. AI Engineer Round 2: Technical round with AI Engineer Manager and Lead AI Engineer Round 3: Salary discussion with HR Manager
Very_easy difficulty · India · 82 reports
More interview insightsJob description
Overview
The Principal Machine Learning Engineer is a senior technical leader responsible for architecting, building, and operationalizing enterprise-scale AI, Generative AI, and Agentic AI capabilities across Databricks, Azure AI Foundry, and cloud-native AI platforms.
Responsibilities
- Own the enterprise strategy for foundation models, frontier models, and custom enterprise models.
- Evaluate, benchmark, onboard, and operationalize leading AI models from OpenAI, Anthropic, Google, Azure AI Foundry, Databricks Mosaic AI, and open-source ecosystems.
- Define model selection and deployment strategies based on performance, cost, security, latency, and business requirements.
- Establish enterprise standards for model consumption and governance.
- Architect and implement end-to-end model lifecycle management capabilities.
- Lead model training, fine-tuning, evaluation, testing, deployment, monitoring, optimization, and retirement processes.
- Build automated ModelOps and MLOps pipelines to support enterprise-scale AI workloads.
- Implement model versioning, lineage, experimentation tracking, model monitoring, and drift detection frameworks.
- Ensure reproducibility, compliance, governance, and auditability of AI models.
- Design and implement enterprise Agentic AI architectures and frameworks.
- Develop multi-agent orchestration patterns, planning frameworks, tool integration, reasoning workflows, memory management, and contextual intelligence capabilities.
- Define AgentOps standards for deployment, monitoring, evaluation, and governance of autonomous agents.
- Establish reusable enterprise frameworks supporting scalable agent development and deployment.
- Architect enterprise AI Gateway capabilities for secure model access and governance.
- Design LLM routing frameworks that dynamically select optimal models based on workload, cost, latency, and performance requirements.
- Define model consumption patterns for applications, APIs, copilots, and intelligent agents.
- Enable centralized access, governance, monitoring, and observability across all AI services.
- Develop abstraction layers supporting seamless integration of multiple foundation models.
- Define token optimization strategies to improve AI cost efficiency and performance.
- Implement prompt engineering, caching, model tiering, response optimization, and intelligent routing techniques.
- Establish monitoring and reporting frameworks for model utilization, token consumption, and AI infrastructure costs.
- Drive AI FinOps initiatives and platform optimization strategies.
- Build and scale AI platform capabilities on Databricks, Azure AI Foundry, and Azure cloud platforms.
- Architect enterprise-ready model serving, inference, vector search, RAG, and semantic retrieval solutions.
- Develop reusable AI platform services, accelerators, SDKs, and reference architectures.
- Enable secure and governed AI consumption across multiple business domains.
- Design cloud-native infrastructure for model training, fine-tuning, and large-scale inference workloads.
- Build GPU-enabled, highly scalable, resilient, and secure AI environments.
- Implement Infrastructure as Code, automated deployments, and platform observability.
- Establish security-by-design principles for AI workloads including model security, access controls, secrets management, and responsible AI controls.
Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.
- 12+ years of experience in Software Engineering, Machine Learning, AI Platform Engineering, or Cloud Architecture.
- 5+ years of experience leading enterprise AI/ML platform implementations.
- Proven experience deploying and operating large-scale AI, LLM, and Agentic AI solutions in production environments.
- Successful operationalization of frontier and custom foundation models.
- AI platform scalability, reliability, security, and governance compliance.
- Reduced model deployment timelines through automated ModelOps capabilities.
- Optimized AI infrastructure and token consumption costs.
- Adoption of reusable AI platform patterns across business domains.
- Increased speed, quality, and business impact of AI and Agentic AI solutions
- Establishment of enterprise-grade AI architecture standards and engineering excellence.
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Job application FAQ
Last updated 22 September 2026
- How do I apply for Principal AI Engineer — ML / MLOps Platform Architect at Ecolab Inc.?
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- Is Principal AI Engineer — ML / MLOps Platform Architect remote or on-site in Bengaluru, Karnataka?
- This listing is marked on_site with location Bengaluru, Karnataka. Confirm details on the job description before applying.
- What skills matter for this Principal AI Engineer — ML / MLOps Platform Architect role at Ecolab Inc.?
- This is a principal Principal AI Engineer — ML / MLOps Platform Architect role. Key skills include skills listed in the job description. Feedback AI highlights matched vs missing skills on your profile after signup.