Generative AI Engineer

Kubernetes/OKE Operations & Optimization

• Provision, configure, and maintain production OKE clusters (node pools, autoscaling, network policies, RBAC).
• Conduct capacity‑planning, performance tuning, and fault‑tolerant design to guarantee high availability and low latency for AI-enabled services.
• Integrate static code analysis, container image scanning (e.g., Trivy), automated unit/integration tests, and artifact promotion stages.
• Enforce code‑quality gates and version‑controlled releases for all AI platform components.

• Integrate static code analysis, container image scanning (e.g., Trivy), automated unit/integration tests, and artifact promotion stages.
• Enforce code‑quality gates and version‑controlled releases for all AI platform components.
• Coordinate functional, performance, and bias testing scripts.
• Approve model‑sign‑off packages before they move to staging.

• Trigger CI/CD releases for models, verify successful rollout, and confirm rollback procedures are in place.

• Maintain real‑time monitoring dashboards; review daily/weekly drift reports and initiate retraining tickets when thresholds are breached.

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related discipline (Master’s degree preferred).
• 3 –8 years of hands‑on experience operating and optimizing production‑grade Kubernetes clusters (Oracle Kubernetes Engine OKE or equivalent) that host AI or platform services.
• Proven track record designing, building, and maintaining highly available, scalable, and secure containerized environments.
• Job‑Specific
  
  Skills:
  
  
• Kubernetes/OKE Operations:
  Cluster provisioning, node pool management, autoscaling, network policies, RBAC, secret management, and disaster‑recovery design.
• CI/CD Automation:
  Pipeline design with integrated static code analysis, unit/integration testing, container image scanning and staged artifact promotion.
• Observability & Incident Management:
  Configuration of metrics, alerts, dashboards, log aggregation, distributed tracing, and on‑call rotation processes.
• Security & Compliance:
  Implementation of pod security standards, vulnerability remediation, image signing, and compliance checks aligned with internal and regulatory requirements.
• Infrastructure‑as‑Code:
  Proficient in Terraform, Helm charts, and Git Ops practices to ensure reproducible, auditable infrastructure changes.
• Scripting & Programming:
  Strong scripting abilities in Bash/Python/Go for automation, tooling, and custom operators.
• Performance Tuning:
  Capacity planning, resource quota management, latency optimization, and cost‑efficient scaling of AI workloads.
• Collaboration Tools:
  Experience with version‑control systems (Git), issue‑tracking (Jira), and documentation platforms (Confluence, Markdown) to maintain clear knowledge sharing;
  Monitoring & Reliability:
  Setting up logging, metrics, and alerting (e.g., Prometheus, Grafana) to ensure fault‑tolerant operations and rapid incident response.

Consistently delivers projects on schedule, within budget, and to quality standards while managing competing priorities.

• Influence & Collaboration

  Builds strong relationships across business, technical, and compliance teams; secures consensus and drives collective ownership of outcomes.

• Analytical Decision‑Making

  Uses data, risk analysis, and stakeholder input to make timely decisions, even when information is incomplete.

• Customer‑Focused Delivery

  Prioritises the needs of internal and external customers, ensuring solutions are usable, valuable, and meet defined business objectives.

• Ethical AI Stewardship

  Applies responsible AI principles, proactively identifying bias or compliance concerns and embedding mitigation measures throughout delivery.

• Clear Communication

  Translates complex technical details into concise, business‑oriented language for diverse audiences; facilitates transparent information flow.