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Principal AI Engineer

Ecolab
Bengaluru, IND
Full-time
Senior · 10–12 years experience
Onsite
Discovered 3 weeks ago
azuredatabricksdockergeminikubernetesopenai

Full Job Posting

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.

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.

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