Platform Engineer - Deployment Team

Job Overview

We are seeking a Platform Engineer to join our Deployment team at blackshark.ai. This remote engineering position requires working within European hours and involves building and operating infrastructure that powers our AI platform across various environments, including AWS, single-tenant cloud deployments, K3S clusters, and edge deployments.

This role offers unique deployment challenges and strategic impact, working with defense and government customers while maintaining a strong technical culture and healthy work-life balance.

Key Responsibilities

• Build and operate Kubernetes clusters across AWS, Azure, GCP and bare-metal K3S deployments


• Own infrastructure as code using Terraform, evolving our multi-environment architecture


• Design and improve CI/CD pipelines to accelerate the path from code to production


• Enhance observability across the stack with Prometheus, Grafana, and the ELK stack


• Maintain security standards required for defense and government customers


• Optimize cloud costs while ensuring performance and reliability


• Travel to customer sites to support on-premises deployments and troubleshoot production issues


• Ensure testing and staging environments are reproducible and consistent with production

Required Qualifications

• Experience with Docker, Kubernetes, and Terraform in production environments


• Strong Linux system administration skills with the ability to debug production issues


• Solid understanding of networking, security, and infrastructure fundamentals


• Experience with CI/CD pipelines and deployment automation


• Good communicator who enjoys working in a team and can context-switch across priorities


• Comfortable traveling to customer sites for on-premises deployments


• Scripting proficiency in Python and Bash
• Ideally: Experience with multi-environment or multi-tenant deployment architectures


• Background in regulated environments (government, defense)


• Familiarity with GPU infrastructure and ML workload scheduling


• Experience with K3S or lightweight Kubernetes distributions for edge deployments


• Knowledge of container security scanning and hardening practices

Tech Stack

Linux, Docker, Kubernetes (EKS, K3S), AWS, Azure, GCP, Terraform, Azure DevOps, ArgoCD, Helm, Prometheus, Grafana, Elasticsearch, Kibana, Redis, PostgreSQL, Python, Bash