Principal AI Engineer - Enterprise AI Platform Lead

Ecolab Food Safety & Hygiene Solutions Private Limited; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Ecolab Food Safety & Hygiene Solutions Private Limited in Bengaluru is seeking a Principal AI Engineer to lead the enterprise Agentic AI Platform, defining vision, roadmap, and operating model for development, deployment, and management of AI agents at scale.

You will oversee orchestration, model lifecycle, cost management, security, and governance while driving platform adoption across business domains and ensuring reliability and compliance.

Qualifications

• 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.

Responsibilities

• Define and execute vision, roadmap, and operating model for enterprise Agentic AI Platform.
• Lead platform engineering teams for Agentic AI infrastructure, tooling, and support.
• Drive platform adoption across domains and enterprise AI initiatives.
• Design and manage orchestration layer for AI agents and integration with systems and data sources.
• Own enterprise LLM services, model lifecycle, benchmarking, deployment, and cost management.
• Define AI governance frameworks, controls for Responsible AI, risk management, and compliance.
• Establish AI security architecture and controls, IAM and secret management.
• Establish monitoring, SLAs, SLOs, incident management, and platform reliability.
• Lead a team of AI Platform Engineers, MLOps Engineers, AI Security Specialists, and Platform Administrators.

Education

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

Tools

LangGraph

CrewAI

AutoGen

Semantic Kernel

OpenAI Agents

Azure OpenAI

OpenAI

Anthropic

Gemini

Open Source Models

Kubernetes

Docker

API Gateways

Databricks

Azure AI Foundry

Snowflake

Security by Design

Zero Trust Architecture

DevSecOps

Job description

Principal AI Engineer — AI & Data Platform

Key Responsibilities

• 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.
• Orchestrator Layer & Multi-Agent Framework 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. Ensure compliance with organizational security standards, privacy regulations, and industry best practices.
• Observability & Platform Operations Establish comprehensive monitoring for agent performance, model behavior, platform reliability, and operational health. Define SLAs, SLOs, and platform support processes. Build automated incident management and operational response capabilities. Drive continuous improvement through platform telemetry and analytics.
• Stakeholder & Team Leadership 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. Manage vendor relationships and strategic technology partnerships.

Qualifications

• 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.

Preferred Technical Skills

• 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
• Security by Design and Zero Trust Architecture
• AI Governance and Responsible AI Frameworks
• DevSecOps and Platform Engineering Practices

Success Metrics

• 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.

Leadership Profile

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.

Experience Level

Senior Level