Job Characteristics: Define the architectural vision and governance standards for GenAI solutions at scale. Lead the design of intelligent systems leveraging LLMs, agentic workflows, and vector-based retrieval strategies, while ensuring alignment with enterprise-level goals around security, performance, and reusability. Education/Work Experience: Degree and 10-12 years experience. Independence Level/Reports to: Normally reports to AI Engineering Manager or Director.

The Principal Engineer – Agentic AI Platform will lead the strategy, architecture, governance, security, and operational management of the enterprise Agentic AI Platform. This role is responsible for building and managing the foundational platform capabilities required to scale AI Agents across the enterprise, including agent orchestration frameworks, LLM platform services, AI security controls, governance frameworks, observability, and operational excellence.

The individual will work closely with Enterprise Architecture, Data & AI teams, Security, Platform Engineering, and Business stakeholders to establish a secure, scalable, and governed Agentic AI ecosystem that accelerates AI adoption while ensuring compliance, reliability, and responsible AI practices.

Agentic AI Platform Leadership

Define and execute the vision, roadmap, and operating model for the enterprise Agentic AI Platform.

Establish platform capabilities that enable the development, deployment, and management of AI Agents at scale.

Lead platform engineering teams responsible for Agentic AI infrastructure, tooling, and operational support.

Drive platform adoption across business domains and enterprise AI initiatives.

Design and manage the enterprise orchestration layer for AI agents.

Establish standards for agent-to-agent communication, workflow orchestration, task planning, memory management, and tool integration.

Build reusable frameworks that accelerate agent development and deployment.

Enable integration with enterprise systems, APIs, data sources, and workflow platforms.





LLM Platform Management

Own enterprise LLM services and model infrastructure.

Evaluate, onboard, and manage proprietary and open-source foundation models.

Establish model lifecycle management processes, including model selection, benchmarking, deployment, monitoring, and optimization.

Drive LLM cost management, performance optimization, and reliability engineering.

AI Governance & Responsible AI

Define and implement AI governance frameworks, policies, and operating procedures.

Establish controls for Responsible AI, model risk management, auditability, explainability, and regulatory compliance.

Partner with Legal, Risk, Compliance, and Information Security teams to ensure AI solutions meet enterprise requirements.

Create governance reporting and executive dashboards for AI platform adoption and risk management.

AI Security & Compliance

Define enterprise AI security architecture and controls.

Implement safeguards against prompt injection, jailbreak attacks, data leakage, model abuse, and unauthorized access.

Establish identity, access management, and secret management controls for AI services.

Observability & Platform Operations

Establish comprehensive monitoring for agent performance, model behavior, platform reliability, and operational health.

Lead a team of AI Platform Engineers, MLOps Engineers, AI Security Specialists, and Platform Administrators.

Collaborate with Enterprise Architects, Data Engineering, Security, and Business stakeholders.

Provide technical leadership, mentoring, and talent development.

Required Experience

- Bachelor's or Master's degree in Computer Science, Engineering, Data Science,



or related field.
- 10+ years of experience in software engineering, AI/ML platforms, cloud platforms, or enterprise platform management.
- 5+ years of leadership experience managing engineering or platform teams.
- Experience building and operating enterprise AI/ML platforms.
- Hands-on experience with LLMs, Generative AI, Agentic AI frameworks, and cloud-native architectures.

Agentic AI Frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents, etc.)

LLM Platforms (Azure OpenAI, OpenAI, Anthropic, Gemini, Open Source Models)

MLOps and AI Operations

Kubernetes, Docker, API Gateways

Databricks, Azure AI Foundry, Snowflake

AI Governance and Responsible AI Frameworks

DevSecOps and Platform Engineering Practices

Enterprise adoption of Agentic AI Platform.

Reduction in time to deploy production-grade AI agents.

AI platform reliability, scalability, and operational efficiency.

Compliance with AI governance and security standards.

Successful implementation of Responsible AI controls.

Optimized LLM usage, performance, and cost management.

Improvement in developer productivity and AI solution delivery velocity.

A strategic technology leader who combines platform engineering expertise, AI innovation, governance discipline, and operational excellence to build a secure, scalable, and enterprise-grade Agentic AI ecosystem.

Our Commitment to a Culture of Inclusion & Belonging

We will recruit, hire, promote, transfer and provide opportunities for advancement based on individual qualifications and job performance in all matters affecting employment, compensation, perks, 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.

📌 Principal AI Platform Engineer (Bengaluru)
🏢 Ecolab
📍 Bengaluru