Principal AI Engineer — ML / MLOps Platform Architect

Ecolab; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Ecolab in Bengaluru seeks a Principal Machine Learning Engineer to architect and operationalize enterprise-scale AI platforms, focusing on LLMs, Agentic AI, and ModelOps. You will lead strategy, governance, and cross‑functional delivery across cloud platforms, data pipelines, and AI services.

The role demands deep ML expertise, hands-on software engineering, and strong leadership to drive scalable, cost-efficient AI adoption while ensuring compliance and security across the organization.

Qualifications

• Extensive experience in ML platform engineering and enterprise AI.
• Proven ability to lead large-scale AI/ML projects.
• Experience with model governance and MLOps.

Responsibilities

• Lead enterprise LLM platform strategy and governance.
• Architect end-to-end ModelOps pipelines.
• Oversee agentic AI architectures and multi-agent orchestration.
• Drive secure, scalable AI deployments.

Skills

MLOps

Platform engineering

Cloud architecture

Leadership

Education

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field

Tools

Databricks

Azure Cloud

Azure AI Foundry

Job description

Job Description – Principal Machine Learning Engineer (LLM, Agentic AI & Model Platform)

Position Summary

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. This role will lead the strategy, architecture, and implementation of foundation models, custom models, AI platform services, ModelOps, and Agentic AI frameworks that power enterprise AI solutions.

The ideal candidate combines deep machine learning expertise with hands‑on software engineering, cloud architecture, MLOps, and platform engineering skills. They will drive model lifecycle management, AI gateway architecture, model routing strategies, token optimization, and enterprise AI governance while enabling secure, scalable, and cost‑efficient AI adoption across the organization.

Key Responsibilities

Enterprise LLM Platform Leadership

• 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.

Model Lifecycle Management (ModelOps)

• 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.

Agentic AI Architecture

• 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.

AI Gateway & Model Routing

• 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.

Token Optimization & AI FinOps

• 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.

AI Platform Engineering

• 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.

Cloud Infrastructure & Security

• 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.
• Collaborate with Security and Governance teams to ensure compliance with enterprise standards.

Technical Leadership

• Serve as the principal technical authority for Machine Learning, LLMs, Agentic AI and Model Platforms.
• Mentor AI Engineers, ML Engineers, Platform Engineers, and Architects.
• Lead architecture reviews, platform strategy discussions, and technology evaluations.
• Drive innovation through proof‑of‑concepts and adoption of emerging AI technologies.
• Partner with business and technology leaders to accelerate AI transformation initiatives.

Required Technical Skills

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.

Success Metrics

• 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.