Generative AI Engineer

1.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.
– Implement routine health‑checks, node‑drain procedures, and disaster‑recovery drills.

2.CI/CD Pipeline Management:

– Design, build, and maintain end‑to‑end build‑test‑deploy pipelines using Jenkins, GitLab CI, GitHub Actions, or Argo CD.
– 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.

3.Observability & Incident Response:

– Design, build, and maintain end‑to‑end build‑test‑deploy pipelines using Jenkins, GitLab CI, GitHub Actions, or Argo CD.
– 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.

4.Model Testing & Validation:

– Coordinate functional, performance, and bias testing scripts.
– Approve model‑sign‑off packages before they move to staging.

5.Production Deployment Support :

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

6.Performance Monitoring:

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

Minimum Qualifications:

– 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 GitOps 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.

– Execution Excellence
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.