Engineer Manager - Agentic AI / Agent Identity

The Engineering Manager, AI is positioned as an engineering leadership role for teams building enterprise-grade AI security, governance, and agent-management platforms. The role combines engineering leadership with technical depth across: AI/ML systems and LLMs, AI agents, distributed systems, Cloud-native architecture, Security, Scalability and reliability, and engineering execution

The candidate will have responsibilities across the following functions:

Engineering Leadership:

• Build, mentor and manage 15-30 software engineers, architects and technical leads.
• Drive software engineering practices, architecture reviews, code quality and operational rigour.
• Develop engineering talent while promoting ownership, accountability and customer focus.

AI Platform, Architecture, Governance and Security:

• Lead enterprise AI-native SaaS platforms covering AI governance, compliance, policy enforcement, MCP security, AI risk management and security analytics.
• Shape architecture across LLM integrations, AI gateways/proxies, security enforcement, event-driven architecture, and workflow orchestration.
• Establish secure AI practices around policy enforcement, prompt security, agent/MCP governance, audit logging, and observability.
• Drive reliability, performance, scalability, and security.

Reliability and Operations:

• Establish observability, monitoring, incident management, and SRE practices.
• Define SLAs/SLOs and reliability goals.
• Drive production readiness and post-incident improvement.
• Drive cloud-cost and platform-efficiency initiatives.

Requirements:

• 12+ years of software engineering experience.
• 4+ years leading engineering teams in product development.
• Enterprise-scale SaaS and distributed-system experience.
• Strong Generative AI, LLM, AI-agent, and AI application architecture expertise.
• Experience designing/integrating AI platforms, model services, APIs, and enterprise AI workflows
• Microservices, Kubernetes, containers, and event-driven architecture.
• AWS/Azure/GCP knowledge, including DevOps and observability.
• AI/security governance and compliance exposure.
• Ability to lead technical execution and mentor engineering leaders.