Network DevOps Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Principal Network DevOps Engineer based in India.

• Design, develop, and deploy AI-powered operational platforms that modernize network and cloud infrastructure management. Build intelligent assistants, decision-support systems, and autonomous or semi-autonomous workflows capable of multi-step reasoning and execution. Implement AI-driven troubleshooting, incident triage, root cause analysis, diagnostics, and operational insights to reduce manual effort and improve service reliability.
• Develop enterprise AI capabilities using large language models (LLMs), Retrieval-Augmented Generation (RAG), and context-aware knowledge retrieval systems. Integrate information from documentation, operational records, runbooks, policies, and enterprise applications to improve search, summarization, recommendations, and technical decision-making.
• Lead the design, deployment, operation, and optimization of global network infrastructure and cloud connectivity platforms. Define technical architecture, roadmaps, and network standards while ensuring infrastructure deployments meet security, reliability, scalability, and performance requirements. Contribute to strategic backbone networking initiatives and manage enterprise-scale Secure Access Service Edge (SASE) solutions.
• Develop scalable automation frameworks using Python, APIs, Terraform, AWS CloudFormation, and related technologies. Build reusable automation services, integration components, software development kits, and orchestration capabilities. Automate network provisioning, configuration validation, compliance assessments, governance enforcement, and remediation workflows.
• Implement modern engineering practices across network platforms and automation services, including GitOps, continuous integration, and continuous delivery pipelines. Establish reusable design patterns, coding standards, and infrastructure-as-code best practices while enabling self-service automation for engineering and operations teams.
• Design and implement secure integrations between cloud platforms, enterprise applications, security tools, IT service management systems, monitoring platforms, and knowledge repositories. Develop API-first, event-driven integration frameworks and workflow orchestration services that support reliable data exchange across distributed systems.
• Build automated controls for governance, risk management, compliance, and audit requirements. Implement intelligent approval workflows, policy enforcement, traceability, audit trails, and accountability mechanisms. Ensure AI-driven solutions comply with enterprise security, privacy, and regulatory requirements, incorporating human approval for sensitive or high-risk operational actions.
• Develop comprehensive observability and telemetry platforms using metrics, logs, traces, and events. Create dashboards, alerts, health monitoring, and performance reporting capabilities. Implement anomaly detection, predictive analytics, and AI-driven insights to enable proactive issue detection, improve operational visibility, and optimize infrastructure performance.
• Define the technical vision and architecture for network automation, cloud connectivity, and AI-enabled operational platforms. Lead large-scale infrastructure initiatives, document technical designs and policies, influence engineering decisions across multiple teams, and ensure alignment with enterprise architecture standards.
• Partner with engineering, security, architecture, cloud, platform, operations, and business teams to deliver scalable and resilient solutions. Participate in Agile delivery processes, manage long-term initiatives and project milestones, communicate technical recommendations to stakeholders, and support enterprise-wide adoption of AI-driven operational capabilities.
• Evaluate emerging AI technologies, automation frameworks, and cloud networking capabilities. Identify opportunities to improve engineering productivity, operational resilience, and service quality while establishing responsible AI practices and scalable implementation standards.

• Bachelor's degree in Computer Science or a related discipline, or equivalent practical experience.
• At least 7 years of experience in software engineering.