The Senior DevOps Engineer is a core execution driver of Mercans’ AI-native infrastructure strategy and engineering automation. Reporting to the CTO and collaborating across Product, Engineering, Data Science, and SRE, this role translates architectural vision into secure, scalable, and automated platform operations.
The focus is on enabling hyperscale payroll processing and proprietary AI model training/inference within a secure, cost-efficient private cloud environment, while accelerating product delivery through GitLab-powered DevSecOps automation.
- Design and maintain GitLab CI/CD pipelines supporting rapid feature delivery, A/B testing, feature flagging, blue-green and canary deployments.
- Embed shift-left security (SAST, DAST, dependency, container, IaC scanning, secret detection) into merge requests and deployment gates.
- Provide self-service deployment templates and pipeline libraries to reduce developer friction.
- Standardize release orchestration for applications and AI models using GitLab DevSecOps workflows.
- Enforce architectural, security, and compliance controls defined by Enterprise Architecture.
- Automate infrastructure provisioning using Infrastructure as Code (Terraform or equivalent) aligned with AI cloud architecture.
- Operate and optimize Kubernetes clusters, including GPU-enabled workloads for AI/ML training and inference.
- Implement autoscaling, bin-packing, fractional GPU scheduling, and runtime security policies.
- Scan infrastructure and container workloads for vulnerabilities and misconfigurations.
- Implement logging, metrics, and tracing to support high availability targets and multi-datacenter resilience.
- Apply SRE practices including SLIs/SLOs, error budgets, incident response, and postmortems.
- Contribute to chaos testing and resilience engineering.
- Use GitLab security dashboards and conditional pipelines for secure testing and remediation.
- Embed security policies into CI/CD pipelines.
- Enforce RBAC, secrets management, and compliance-aware deployment standards.
- Ensure secure delivery of regulated, data-sensitive payroll and AI workloads.
- Develop runbooks, deployment standards, and reusable pipeline templates.
- Support internal Centers of Excellence for SRE and AI Engineering.
- Drive DevOps maturity and best practices across Product and Engineering teams.
- 4–6+ years of experience in DevOps, SRE, or Platform Engineering operating production-grade Kubernetes systems.
- Hands-on experience with private cloud or on-prem Kubernetes environments and Infrastructure as Code tools (Terraform, Ansible, or equivalent).
- Experience running GPU-based containerized workloads for AI/ML use cases.
- Strong automation and programming skills (Python, Go, or similar).
- Solid understanding of observability stacks and SRE methodologies.
- Knowledge of secure software delivery and compliance-aware deployment in regulated environments.
- Advanced proficiency with GitLab DevSecOps (CI/CD pipelines, security scanning, RBAC, policy enforcement, feature flags).
- Experience enabling engineering teams with self-service platforms and GitOps workflows.
- Strong documentation, communication, and cross-functional collaboration skills.