Senior DevOps Engineer (Cloud & ML Infrastructure)

Your future position

Key Responsibilities

Design, operate, and improve Kpler’s cloud-native infrastructure (Kubernetes, networking, compute, storage).

Contribute to Infrastructure as Code, CI/CD pipelines, and platform automation.

Ensure high availability, reliability, and security of production systems.

Improve observability, monitoring, alerting, and incident response processes.

Reduce MTTR and failure rates through structured reliability improvements.

Optimize infrastructure cost and performance, including compute-intensive workloads.

Support and help standardize ML/GPU-based workloads within the existing platform model.

Collaborate closely with ML engineers, data engineers, and backend teams to ensure production-grade deployments.

Contribute to architectural decisions shaping the evolution of the platform.

Experience & Background

Essential:

5+ years of experience in cloud/platform engineering in production environments.

Strong hands-on experience with Kubernetes in production.

Experience with Infrastructure as Code (Terraform preferred).

Strong knowledge of AWS (or equivalent cloud provider).

Experience operating distributed systems in 24/7 environments.

Strong operational mindset (SLOs, monitoring, incident management).

Desirable:

Proven experience running ML/AI workloads in production.

Experience with GPU-based workloads.

Exposure to LLM-based or compute-intensive systems.

Experience optimizing cost and performance of high-compute infrastructure

Skills & Competencies

Technical / Functional Skills:

Strong cloud platform engineering expertise (AWS preferred).

Advanced Kubernetes operations in production (scaling, upgrades, workload isolation, troubleshooting).

Solid Infrastructure as Code experience (Terraform or equivalent).

Strong understanding of distributed systems and cloud-native architectures.

Experience designing and operating CI/CD pipelines.

Strong observability practices (monitoring, logging, alerting, SLO definition).

Incident management and root cause analysis in 24/7 systems.

Infrastructure cost optimization and performance tuning.

Solid programming skills (Python or Go preferred).

Practical experience supporting ML/AI or GPU-based workloads in production (highly valued).

Behavioural Competencies:

Ownership & Accountability – Takes end-to-end responsibility for production systems and reliability outcomes.

Systems Thinking – Understands architectural trade-offs and long-term impact of technical decisions.

Structured Problem Solving Under Pressure – Maintains clarity and effectiveness during incidents and high-stakes situations.

Collaborative & Autonomy – Communicates clearly in distributed teams, documents decisions effectively, and works autonomously while maintaining strong cross-team alignment

Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent practical experience.

Strong programming skills (Python or Go preferred).

Solid understanding of cloud-native architecture and reliability engineering principles.

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