Overview

Lead reliability engineering for business-critical manufacturing platforms, owning SLOs, observability, CI/CD, infrastructure automation, incident management and production readiness.

Responsibilities

• Define SLI/SLO/SLA and error budgets; align release velocity with reliability targets.
• Build observability across metrics, logs and traces using Splunk, Datadog, Prometheus and Grafana.
• Create actionable alerts and dashboards that reduce alert fatigue.
• Develop Jenkins CI/CD pipelines and evaluate GitOps/Argo CD.
• Manage GCP infrastructure with Terraform; operate Docker/Kubernetes workloads.
• Conduct load/stress testing and production-readiness reviews.
• Own incident response, on-call, MTTA/MTTR, postmortems, runbooks and improvement.
• Monitor model-serving, agent orchestration and RAG systems for availability, latency, task success and drift.
• Define AI SLOs and safeguards including auditing, human-in-the-loop escalation and runaway-agent circuit breakers.
• Implement AIOps for anomaly detection, predictive alerts, automated triage and GenAI-assisted RCA/runbooks.
• Reduce operational toil through automation; collaborate across platform, manufacturing, data/AI and IT teams; mentor engineers.
• Provide weekly reliability, incident and KPI reporting.

Qualifications

• Open Positions: 01
• Role Summary
• Lead reliability engineering for business-critical manufacturing platforms, owning SLOs, observability, CI/CD, infrastructure automation, incident management and production readiness. Lead AI/Agentic-AI reliability covering model/agent observability, AIOps, drift monitoring, workflow guardrails and GenAI-assisted incident response.
• 5–8+ years in SRE, DevOps or platform engineering.
• Strong SLI/SLO, error-budget, observability and incident-management expertise.
• Strong GCP experience including networking, compute, managed services and access.
• Production experience with Kubernetes, Docker and Terraform.
• Jenkins CI/CD experience; GitOps/Argo CD exposure desirable.
• Splunk plus Datadog, Prometheus or Grafana experience.
• Python and/or Java scripting capability.
• Load/stress testing, capacity analysis and production-readiness experience.
• PagerDuty, Opsgenie or equivalent on-call tooling knowledge.
• Exposure to AI/ML reliability, MLOps, AIOps or Agentic-AI operations.
• Vertex AI: model monitoring, pipelines and agent-building services.
• LangChain, LangGraph or comparable agent orchestration frameworks.
• RAG, vector search and retrieval-system observability.
• Model performance and data-drift monitoring.
• Inference latency, model availability and agent task-completion observability.
• GenAI-assisted incident response, runbook retrieval, incident copilots and RCA summarisation.
• Technical leadership and architectural decision-making.
• Analytical troubleshooting of distributed and AI-system failures.
• Clear communication of reliability, risks and progress to technical/executive audiences.
• Cross-functional collaboration and SRE mentoring.
• Operational ownership through incident closure, corrective actions and documentation.
• SLO/error-budget framework and integrated observability dashboards.
• Controlled CI/CD and Terraform-based infrastructure automation.
• Incident-management process, on-call model, runbooks and postmortems.
• Load/stress testing evidence for production readiness.
• Initial AI/Agentic-AI observability and AIOps/GenAI incident-response proof of concept.
• Modular onboarding architecture and regular reliability/KPI reporting.