Engineering Manager

DDN; Pune District; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

Doist is seeking an Engineering Manager - Control Plane to lead the design and development of Manageability solutions for a data platform enabling centralized control, automated operations, and intelligent support across on-prem and cloud environments.

You will guide a team delivering cloud-native, API-first, and AI/ML-powered systems focused on scalability, reliability, automation, and innovation at petabyte scale.

Qualifications

• 15+ years of experience in software engineering, distributed systems, or cloud platforms.
• 5+ years in technical leadership or management roles.
• Proven experience building large-scale platform management or infrastructure systems.
• Strong background in distributed systems architecture and cloud-native technologies.
• Experience with APIs, microservices, and infrastructure-as-code (IaC).
• Familiarity with AI/ML concepts applied to operational analytics or automation.
• Experience managing teams delivering production-grade, enterprise-scale systems.
• Experience in storage systems, data platforms, or high-performance computing environments.
• Background in building AI-driven operations or AIOps platforms.
• Experience with hybrid cloud and OnPrem deployments.
• Knowledge of security, compliance, and enterprise governance requirements.
• Familiarity with DevOps, SRE practices, and CI/CD pipelines.

Responsibilities

• Lead a high-performing engineering team across distributed systems, cloud infrastructure, and AI/ML.
• Collaborate with cross-functional teams to align platform capabilities with business and customer needs.
• Establish engineering best practices, development standards, and operational excellence frameworks.
• Implement policy-driven infrastructure management and Infrastructure-as-Code (IaC) frameworks.
• Develop self-service tooling and RBAC for enterprise customers.
• Design API-first management interfaces for integration with external tools and automation workflows.
• Drive proactive capacity planning and performance optimization for large-scale deployments.
• Build self-healing systems that reduce manual intervention and improve system resilience.
• Develop predictive analytics capabilities for capacity planning, performance forecasting, and failure prevention.
• Integrate intelligent recommendations and prescriptive insights into operational workflows.
• Define and enforce an API-first, cloud-native architecture across all components.
• Ensure systems are highly scalable, resilient, secure, and capable of operating at petabyte scale.
• Promote automation-first principles across development, testing, deployment, and operations.
• Oversee the design of distributed systems with high availability and fault tolerance.

Skills

Leadership

Distributed systems

Cloud-native technologies

APIs

IaC

RBAC

Self-service tooling

AI/ML concepts

SRE/DevOps

Capacity planning

Tools

Terraform

Kubernetes

CI/CD pipelines

Job description

As an Engineering Manager - Control Plane, you will lead the design and development of Manageability solutions for the DDN Infinia AI Data Platform. This role is responsible for building foundational capabilities that enable centralized control, automated operations, and intelligent support across large-scale hybrid (OnPrem + cloud) environments. You will lead a team delivering cloud-native, API-first, and AI/ML-powered systems that ensure operational excellence, proactive incident management, and seamless user experiences at petabyte scale. This is a ground-up platform leadership role focused on scalability, reliability, automation, and innovation.

Key responsibilities

• Lead a high-performing engineering team across distributed systems, cloud infrastructure, and AI/ML.
• Collaborate with cross-functional teams (product, engineering, SRE, security, and customersuccess) to align platform capabilities with business and customer needs.
• Establish engineering best practices, development standards, and operational excellenceframeworks.
• Implement policy-driven infrastructure management and Infrastructure-as-Code (IaC)frameworks.
• Develop self-service tooling and role-based access control (RBAC) for enterprise customers.
• Design API-first management interfaces for integration with external tools and automationworkflows.
• Drive proactive capacity planning and performance optimization for large-scale deployments.
• Build self-healing systems that reduce manual intervention and improve system resilience.
• Develop predictive analytics capabilities for capacity planning, performance forecasting, andfailure prevention.
• Integrate intelligent recommendations and prescriptive insights into operational workflows.
• Define and enforce an API-first, cloud-native architecture across all components.
• Ensure systems are highly scalable, resilient, secure, and capable of operating at petabytescale.
• Promote automation-first principles across development, testing, deployment, andoperations.
• Oversee the design of distributed systems with high availability and fault tolerance.

Qualifications

• 15+ years of experience in software engineering, distributed systems, or cloud platforms
• 5+ years in technical leadership or management roles
• Proven experience building large-scale platform management or infrastructure systems
• Strong background in distributed systems architecture and cloud-native technologies
• Experience with APIs, microservices, and infrastructure-as-code (IaC)
• Familiarity with AI/ML concepts applied to operational analytics or automation
• Experience managing teams delivering production-grade, enterprise-scale systems
• Experience in storage systems, data platforms, or high-performance computing environments
• Background in building AI-driven operations or AIOps platforms
• Experience with hybrid cloud and OnPrem deployments
• Knowledge of security, compliance, and enterprise governance requirements
• Familiarity with DevOps, SRE practices, and CI/CD pipelines

Success Metrics

• Delivery of a unified management platform at scale
• Reduction in incident response and resolution times through automation
• Increased system uptime and reliability (zero or near-zero disruption)
• Adoption of self-service and automated operational workflows by customers
• High customer satisfaction and operational efficiency across deployments