Engineer Manager - Agentic AI / Agent Identity

AppViewX; Bengaluru; On-site; INR 4,500,000 - 7,000,000; Full time

Job summary

AppViewX is seeking an Engineering Manager, AI to lead teams building enterprise-grade security, governance, and AI-agent platforms. You will combine leadership with deep technical focus on AI/ML systems, LLMs, distributed cloud-native architectures, and reliability.

You will mentor 15–30 engineers and drive architecture, governance, and security across AI platforms, ensuring scalability, cost efficiency, and production readiness while maintaining strong alignment with customer needs and

Qualifications

• 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.

Responsibilities

• 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.
• 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.
• 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.

Skills

Software engineering leadership

Enterprise SaaS

Distributed systems

Generative AI / LLM

AI agents architecture

Kubernetes / Microservices

Cloud platforms (AWS/Azure/GCP)

DevOps & observability

AI security governance

Mentoring engineering leaders

Tools

Kubernetes

Containers

Event-driven architecture

APIs

Job description

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.