MLOps Platform Engineer

Role Overview

As an MLOps Platform Engineer at dv01, you will design, build, and operate a cloud-native AI infrastructure platform that accelerates AI development across the company while enabling safe and efficient production deployment of AI-powered services.

Key Responsibilities

• Build & Operate AI Infrastructure: Design and operate cloud-native infrastructure and platform tooling that accelerates AI development and enables teams to develop, deploy, and operate AI-powered services in production
• MLOps & DevOps Leadership: Own the operational foundations of AI systems, including CI/CD for AI workloads, scalable inference infrastructure, observability, cost management, and reliability
• Establish Patterns & Services: Create repeatable patterns and shared services that reduce friction for teams building AI-enabled applications
• Enable AI Services & Agents: Build and maintain infrastructure for LLM-backed APIs, Model Context Protocol (MCP) servers, agentic systems, secure tool access, runtime orchestration, and isolation boundaries
• Integrate MLOps into Platform Operations: Apply MLOps concepts to improve platform operations through AI-driven monitoring, alerting, anomaly detection, and incident response
• Governance & Security: Define and implement infrastructure-level governance for AI systems, including access controls, deployment policies, auditability, and secure-by-default patterns
• Technical Leadership: Act as a technical leader influencing platform architecture and best practices; mentor engineers and collaborate with product, data, and application teams

Required Experience

• 8+ years in cloud infrastructure, DevOps, or platform engineering roles with deep expertise designing and operating distributed systems in production
• 5+ years of MLOps/GenAIOps experience (monitoring, anomaly detection, predictive alerting, automated remediation on real production systems)
• Cloud-Native Infrastructure: Proficient in cloud environments, Kubernetes, containerized workloads, and infrastructure-as-code tools (e.g., Terraform)
• AI Workload Support: Hands-on experience supporting platforms that run AI workloads in production