MLOps / AIOps Engineer – Build Scalable AI Platforms

About the role

We are seeking an MLOps / AIOps / LLMOps Engineer to join our multidisciplinary Data & AI team. The role focuses on designing, operating and continuously improving an AIOps platform that runs AI products reliably, at scale and cost‑effectively.

Key responsibilities

• Design, maintain and evolve the AIOps platform for traditional ML models, LLM‑based solutions (RAG pipelines, AI agents) and speech‑analytics use cases.
• Build and operate ML/LLM pipelines with strong emphasis on reliability, automation, observability and drift monitoring.
• Implement LLMOps/AgentOps practices such as evaluation, prompt management, traceability and specialized logging.
• Manage cloud deployments (AWS, Azure) and container orchestration (Docker, Kubernetes) including cost optimisation.
• Set up alerts, dashboards, SLOs/SLIs and basic auto‑remediation mechanisms.
• Collaborate with Data Scientists, Data Engineers and Product teams to productionise robust AI solutions.
• Contribute to internal standards, automation and best‑practice documentation across the AI ecosystem.

Required profile

• Hands‑on experience operating ML systems in production (MLOps, AIOps, LLMOps).
• Solid understanding of LLMOps and AgentOps concepts (RAGs, agents, evaluation, monitoring).
• Experience with AWS and/or Azure in production environments.
• Proficiency with containers, Kubernetes and basic Helm usage.
• Experience building CI/CD pipelines (GitHub Actions, GitLab CI, Azure DevOps, Jenkins or similar).
• Familiarity with observability tools (CloudWatch, OpenTelemetry, Prometheus).
• Infrastructure‑as‑code experience (Terraform, Bicep, CDK or similar).
• Strong Python skills and knowledge of the ML ecosystem (e.g., scikit‑learn, PyTorch).

Required skills

• AWS
• Azure
• Docker
• Kubernetes
• Helm
• CI/CD (GitHub Actions, GitLab CI, Azure DevOps, Jenkins)
• CloudWatch
• OpenTelemetry
• Prometheus
• Terraform
• Bicep
• CDK
• Python
• scikit‑learn
• PyTorch