DevSecOps Engineer

Roles and Responsibilities :

• Build and run COS as a secure cloud-native platform (containers, Kubernetes, OpenStack), using infrastructure/configuration as code. 
• Introduce AI-enabled operations (AIOps) and MLOps/LLMOps practices: automate detection/diagnosis, and set up safe, observable delivery and monitoring of AI components. 
• Execute deployments, upgrades, and configuration changes; troubleshoot by reproducing issues, restoring service, and running performance/load tests as needed. 
• Improve secure CI/CD for platform and services: testing, scanning, policy checks, controlled releases, and compliance (risk assessments, audits, configuration baselines). 
• Apply SRE practices to improve availability, scalability, and performance, and drive proactive monitoring and reliability improvements. 
• Take operational ownership with the team: runbooks, on-call readiness, incident/problem management, SLA follow-up, access management, and service performance reporting. 
  
• Tech stack spans cloud-native microservices (e.g., Go, Svelte, Kubernetes), telemetry backends (e.g., Cortex), and data systems (e.g., Postgres/Cassandra/Kafka).
  

Job Requirements :

• 6+ years in DevSecOps or platform engineering in a cloud-native environment, with end-to-end ownership across build and run. 
  
• Tech stack spans cloud-native microservices (e.g., Go, Svelte, Kubernetes), telemetry backends (e.g., Cortex), and data systems (e.g., Postgres/Cassandra/Kafka).
  
• Strong CI/CD and automation skills, including security checks (SAST/SCA, image scanning, policy checks) and controlled releases. 
• Solid understanding of cloud-native observability (Grafana, Prometheus, Loki) and instrumentation with OpenTelemetry. 
• Comfortable working across a broad tech stack and learning new tools and concepts quickly. 
• Clear communicator who collaborates globally and can explain our work across development, operations, and security. You enjoy mentoring and sharing practices in the team/community. 
• Proactive problem-solver who thrives in hands-on engineering and structured delivery, and enjoys making production systems secure, predictable, and observable. 
• Merit: AI platform engineering (MLOps/LLMOps), deploying/monitoring LLM services, RAG pipelines, and embeddings/vector search. Ericsson experience is a plus.