Principal AI Engineer — AI & Data Platform

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 ResponsibilitiesAgentic 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.