DDN

Manager, Software Engineering

DDN Pune, Maharashtra, India

Software Development · 1,001-5,000 employees

13 h ago
Senior (5-10 yrs) Full-time India
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About the role

Lead and mentor an engineering team to build and maintain scalable control plane and platform services. Drive system architecture, implement AI-assisted engineering workflows, and ensure high-quality delivery across hybrid environments.

What they look for

Software Engineering Distributed Systems Control Plane Kubernetes Go Python Java C++ System Architecture AI Tooling Cloud Infrastructure Observability API Design Team Leadership Reliability Engineering Infrastructure Management

Requirements

Requires a proven history of designing distributed systems and strong coding proficiency in languages such as Go, Python, Java, or C++. Candidates must have hands-on experience with Kubernetes and a track record of leading engineering teams.

Full description

Job Summary

About the Role: We are looking for an Engineering Manager with a strong background in control plane applications to lead a high-impact team building scalable, reliable distributed systems. You will own both the technical direction and the delivery of services that orchestrate, manage, and observe complex infrastructure across cloud and on-premises environments. This is a hands-on leadership role. You will set architecture, raise the engineering bar through your own coding and design contributions, and actively apply AI tooling and agentic workflows to simplify and accelerate how the team designs, builds, tests, and operates software. You will partner closely with development, QA, DevOps, and product teams to ship dependable systems at scale.

Key Responsibilities

•Lead, mentor, and grow a team of engineers building control plane and platform services, owning delivery, quality, and technical health.

•Drive system architecture and design for distributed, highly available services — APIs, schedulers, controllers, reconciliation loops, and state management.

•Stay technically hands-on: contribute to design reviews, prototype critical paths, and write production code where it matters most.

•Champion and operationalize AI-assisted engineering — using LLMs and agentic tooling to simplify development, automate triage, accelerate code review, and reduce toil. DDN Confidential

•Define and uphold engineering best practices across design, code quality, testing, observability, and operational readiness.

•Architect and review Kubernetes-based deployments, ensuring resilient, secure, and scalable workloads across hybrid environments.

•Translate product and business goals into clear technical roadmaps, milestones, and execution plans.

•Collaborate cross-functionally with QA, DevOps, release, and product to ensure reliable, scalable application delivery.

•Make pragmatic build-vs-buy and architecture trade-off decisions, balancing speed, cost, and long-term maintainability.

•Maintain clear documentation of systems, designs, and decisions. Required Skills & Experience

•Proven history designing and building control plane or platform applications — orchestration, distributed coordination, or infrastructure management systems.

•Strong, demonstrated architecture and system design skills, with a track record of shipping complex distributed systems.

  • Strong, proven coding ability in a relevant language (e.g., Go, Python, Java, or C++); able to lead by example in code.
  • Hands-on experience designing and operating Kubernetes workloads in production.
  • Demonstrated use of AI tooling to simplify and improve engineering processes and developer productivity.
  • Experience leading and growing engineering teams while remaining technically engaged.
  • Solid grounding in reliability, scalability, observability, and security fundamentals.
  • Excellent communication, collaboration, and documentation skills.

Nice to Have

  • Domain knowledge in filesystems and storage (e.g., parallel/distributed filesystems, block/object storage, or HPC storage).
  • Experience with high-performance computing (HPC) or large-scale data infrastructure.
  • Experience building or integrating agentic AI systems into production workflows.
  • Familiarity with hybrid cloud and on-premises / airgapped deployment models.