About the role
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.