Cloud Service Security Platform DevOps & Maintenance

Bitdeer Technologies Group • San Jose, CA

8d

About The Position

You are the paved road for every AI product we ship — the CI/CD, IDP, and MLOps substrate that lets model teams deploy without opening a ticket.
This is an entry-to-mid level role
Position Summary
Bitdeer is building an AI-operated GPU cloud, and the AI Cloud team ships the products on top of it. As Cloud Senior DevOps Engineer you build the paved road those products travel down: CI/CD, infrastructure-as-code, MLOps pipelines, and the Internal Developer Platform that lets model, data-science, and product teams deploy at speed without stepping around governance. You are the bridge between research and production, and the operator of the platform substrate the AIOps team plugs into.

Requirements

• Bachelor's degree or above in Computer Science, Engineering, or a related technical field, with 5+ years of hands-on experience in DevOps, Site Reliability Engineering (SRE), or Cloud Infrastructure roles.
• Expert-level knowledge of Linux operating systems and core networking principles (TCP/IP, DNS, HTTP, Load Balancing, VPCs).
• Deep mastery of Docker and Kubernetes orchestration, including a thorough understanding of underlying principles, cluster management, and production-level best practices.
• Proven proficiency in designing and managing infrastructure on major Public or Hybrid Cloud platforms (e.g., AWS, GCP, Azure, Alibaba Cloud), including multi-cloud and hybrid-cloud strategies.
• Strong coding and scripting capabilities in at least one major language (Go, Python, Shell, etc.) with a solid engineering-oriented mindset focused on automation and tooling development.
• Systematic and practical understanding of CI/CD methodologies, Infrastructure as Code (IaC), Observability paradigms, and Site Reliability Engineering (SRE) principles.
• Exceptional problem-solving abilities, sharp technical judgment, and excellent cross-team communication skills to effectively collaborate in a fast-paced, dynamic environment.

Nice To Haves

• Familiarity with MLOps practices, model serving/inferencing frameworks (e.g., vLLM, TGI, Triton Inference Server), and experience managing GPU clusters for AI/ML workloads.
• Proven track record working with large-scale distributed systems or high-concurrency environments (e.g., Fintech, Trading, Real-time processing, or AI platforms).
• Hands-on experience in designing and building Internal Developer Platforms (IDP) to enhance developer autonomy and productivity.
• Deep familiarity with Zero Trust architecture, automated security testing (DevSecOps), and implementing strict compliance frameworks (e.g., SOC2, ISO27001).
• Prior experience acting as a Technical Lead, mentoring junior engineers, or managing DevOps teams.
• You see incidents, tickets, and manual runbooks as source material for the next automation, not as steady-state work. You've either wired an LLM-driven code/config helper into a pipeline or have strong opinions on how to.
• You think in golden paths and defaults, not policies; you'd rather make the right thing easy than write a doc scolding people for doing the wrong thing.

Responsibilities

• CI/CD & MLOps Pipeline Management: Design, implement, and maintain end-to-end CI/CD pipelines for both software applications and machine learning models. Automate build, test, deployment, and rollback processes to ensure seamless transitions from innovation to production.
• Cloud-Native & AI Infrastructure: Build, optimize, and scale cloud-native infrastructure using Kubernetes (K8s) and Docker. Manage and provision specialized computing resources (e.g., GPU clusters) to support high-performance AI workloads and model inferencing.
• High Availability Architecture: Take ownership of high-availability design in production environments. Implement disaster recovery (DR) strategies, self-healing mechanisms, capacity planning, and performance tuning to meet stringent business SLAs.
• Infrastructure as Code (IaC): Champion IaC practices utilizing tools such as Terraform, Ansible, and Helm to achieve fully automated, reproducible, and auditable infrastructure provisioning across multiple cloud environments.
• Observability & Monitoring: Architect and refine comprehensive monitoring, logging, and alerting systems (e.g., Prometheus, Grafana, ELK/EFK stack) to provide deep visibility into system health, application performance, and AI model metrics. Feed the AIOps substrate with clean, well-labeled telemetry.
• Internal Developer Platform (IDP): Build the paved road that lets product, model, and data-science teams deploy without opening a ticket. Make golden paths so obvious that shortcuts feel harder than doing it right.
• Cross-functional Collaboration: Work closely with R&D, Data Science, Security, and Business teams to streamline workflows, eliminate bottlenecks, and continuously elevate engineering efficiency.
• Governance, Security & Compliance: Establish and enforce system stability and security standards. Manage release workflows, implement Zero Trust access controls, oversee secrets management, and ensure compliance (e.g., SOC2, ISO27001).
• Incident Management & Resolution: Act as the technical lead during complex system anomalies and major incidents. Spearhead rapid troubleshooting, thorough RCA, and preventative remediation — and turn each incident into an automation that stops the next one before it pages a human.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level