Principal AI Engineer — AI & Data Platform
At a glance
Senior AI Engineering role at Ecolab. Bangalore · full-time.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Set the platform roadmap and operating approach, then build the capabilities teams need to run AI agents at enterprise scale
- Design agent orchestration standards for communication, planning, memory and tool use, and create reusable development frameworks
- Run LLM services and model infrastructure, covering model onboarding, lifecycle processes, reliability, performance and cost
- Put governance and security controls in place for auditability, responsible AI, compliance and threats such as prompt injection or data leakage
- Monitor agent and platform health, define service targets, improve incident response, and lead engineering teams and partners
What you bring
- A bachelor's or master's degree in computer science, engineering, data science or a related discipline
- At least ten years in software engineering, AI/ML platforms, cloud platforms or enterprise platform management
- At least five years leading engineering or platform teams
- You have built and operated enterprise AI/ML platforms, with hands-on work in LLMs, generative AI, agentic frameworks and cloud-native architecture
Who this fits
This role suits a senior technical leader who can combine platform engineering with AI governance, security and operations. You'll lead AI platform, MLOps, security and administration specialists, while working with architecture, data, security and business teams. The role is based in Bangalore, India.
From the employer
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.
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.