MLOps Platform Engineer

Role Overview

MLOps Platform Engineer at dv01. You will design, build, and operate cloud-native infrastructure and platform tooling that helps teams develop, deploy, and run AI-powered services safely and efficiently in production.

Responsibilities

• Build and operate an AI infrastructure platform to accelerate AI development across the company.
• Own the DevOps/infrastructure side of MLOps and agentic systems, including:
  • CI/CD for AI workloads
  • Scalable inference infrastructure
  • Observability, cost management, and reliability
  • Shared services and repeatable patterns to reduce friction for AI application teams
• Enable AI services, agents, and runtime platforms, such as:
  • LLM-backed APIs
  • Model Context Protocol (MCP) servers
  • Agentic systems used by production applications
  • Secure tool access, runtime orchestration, and isolation boundaries
• Integrate MLOps into platform operations using techniques like:
  • AI-driven monitoring and alerting
  • anomaly detection and incident response
• Establish governance, security, and operational guardrails for AI systems:
  • access controls, deployment policies, auditability, secure-by-default patterns
  • partner with security/compliance to meet risk and regulatory needs
• Provide technical leadership and enablement by influencing architecture and mentoring engineers while partnering with product, data, and application teams.

Requirements

• 8+ years experience in cloud infrastructure, DevOps, or platform engineering; deep expertise operating distributed systems in production.
• 5+ years MLOps experience (required) with direct exposure to ML/GenAIOps practices such as monitoring, anomaly detection, predictive alerting, or automated remediation.
• Strong cloud-native infrastructure skills, including:
  • Kubernetes
  • containerized workloads
  • Infrastructure as Code (e.g., Terraform)
• Hands-on experience supporting platforms that run AI workloads.

Nice-to-haves

• Experience operating AI-enabled platforms at scale with advanced observability and incident response patterns.
• Familiarity with AI runtime governance and secure-by-default deployment practices (access controls, auditability, policy enforcement).