About the Role:

We are looking for a technically strong Engineering Manager to lead a backend engineering pod of 6 - 9 engineers within a high-growth, AI-first product engineering organization.

You will drive engineering excellence, predictable delivery, technical decision-making, and AI-native development practices while building scalable backend systems that power complex business workflows.

Key Responsibilities:

- Lead and mentor a backend engineering team of 6 - 9 engineers.

- Own sprint planning, execution, delivery quality, and engineering productivity.

- Drive code reviews, test coverage, observability, incident management, and engineering best practices.

- Provide technical leadership across system design, architecture, database decisions, and distributed systems.

- Work closely with Product teams to translate business requirements into scalable technical solutions.

- Drive AI-assisted development practices to improve engineering productivity and code quality.

- Own hiring, performance management, career development, and team growth.

- Establish strong engineering standards and promote a culture of ownership and accountability.

Mandatory Skills:

- 8 - 10 years of overall software engineering experience.

- Minimum 2+ years of experience managing engineering teams as an Engineering Manager / Senior Engineering Manager or equivalent.

- Strong hands-on expertise in Java (primary) and/or Python.

- Experience with distributed systems and microservices architecture.

- Strong knowledge of Kafka, MongoDB, PostgreSQL, Redis, and/or Temporal.

- Experience with AWS, Kubernetes, and cloud-based infrastructure.

- Demonstrable experience using AI-assisted development tools and workflows.

- Strong system design, architecture, communication, and stakeholder management skills.

- Proven experience in hiring, mentoring, and developing engineering talent.

Good to Have:

- Experience building or scaling engineering teams.

- Background in high-growth product companies, startups, or enterprise SaaS organizations.

- Exposure to MLOps, LLM observability, and agentic AI systems.

- Experience driving AI-native engineering transformation.

Ideal Candidate:

- Someone who combines strong technical depth with people leadership, can independently drive architectural decisions, remains hands-on with code reviews, and knows how to leverage AI to improve overall engineering productivity.