Engineering Manager - Backend & AI Systems

Hiring for: A US based deep-tech AI startup building the world's first multi-agent cyber studio for Governance, Cyber Security, and Decision Intelligence.

Role: Engineering Manager - Backend & AI Systems

Positions: 1

Experience: 7 to 9 years

Location(s): Mumbai, Nagpur, Pune

Type: Hybrid / Permanent

Salary: Up to INR 30 LPA

Notice Period: 30 days

Key Responsibilities:

- Own execution across five core pillars: product, client issues, infrastructure, compliance, billing

- Act as the technical anchor across teams, not just tracking work, but improving how it gets done

- Identify bottlenecks and drive better engineering decisions across systems

- Stay close to systems — debug, question, and guide teams on architecture, performance, and reliability

- Drive production excellence: latency (P95/P99), system stability, and scalability

- Solve for scaling AI systems in production (moving from manual evaluation to reliable, repeatable systems)

Who This Is For:

- You started your career as a backend engineer and still think like one

- 7–9 years experience building and scaling APIs, microservices, and distributed systems

-You have extremely strong experience in Python

- You have owned production systems, not just shipped features

- You now operate in a role where you: guide engineers, influence technical decisions, drive execution across teams

- You can challenge engineers with better solutions, not just manage timelines

- You are comfortable being hands-on when needed

- You have some exposure to AI systems (LLMs / APIs / agents in production)

This Role Is NOT for You If:

- You do not have hands-on experience in backend engineering

- You have not written or owned backend systems in your early career

- You are looking for program/project management roles

- You have not worked on production systems at scale

- You are not comfortable going deep into technical discussions with engineers

- You prefer clearly defined scopes over ambiguous, evolving problems

What Success Looks Like:

- Engineering teams ship faster with fewer blockers

- Better technical decisions across teams

- Improved system reliability and performance

- Clear visibility into what’s working vs breaking