Senior DevOps / DataOps Engineer (AWS & MLOps)

You are the engineering backbone that enables Centience's teams to build fast and ship confidently. Your primary mission is to eliminate the gap between experimentation and production — designing and operating CI/CD pipelines, MLOps infrastructure, and automated deployment workflows with speed, reliability, and full audit traceability. Day-to-day, you work closely with the Head of Data Science & Analytics to productionise the machine learning models and AI products the business delivers. Your reporting will be to the MD & the Head of Data Science & Analytics. Key Responsibilities 1. CI/CD & MLOps Pipeline Engineering Design, build, and maintain CI/CD pipelines for ML models, data products, and analytical applications using GitHub Actions, Jenkins, or AWS CodePipeline. Implement end-to-end MLOps workflows (MLflow, SageMaker Pipelines, or Kubeflow) covering experiment tracking, model versioning, registry, validation gating, and automated deployment. Automate model evaluation, shadow and A/B testing, and canary/blue-green deployments for zero-downtime releases with full rollback capability.

2. Infrastructure as Code & Environment Automation Own infrastructure-as-code (Terraform and/or AWS CDK) across dev, staging, and production with environment parity. Automate provisioning of ML compute environments (SageMaker Studio, JupyterHub, GPU clusters) so teams can spin up and tear down on demand. Manage containerisation and orchestration (Docker, ECS/EKS), environment isolation, and secrets management (AWS Secrets Manager, HashiCorp Vault).

3. Monitoring, Observability & Reliability Build and maintain observability stacks (CloudWatch, Grafana, Prometheus) for all pipelines, model endpoints, and platform services. Implement model drift detection, production data quality monitoring, and automated alerting for SLA breaches. Define and enforce SLOs/SLAs; lead incident response and post-mortems to eliminate repeat failures.

4. DevSecOps & Compliance Integration Embed security into every pipeline stage — SAST/DAST scanning, dependency and container image checks, and compliance policy enforcement. Ensure deployment artifacts are immutable, signed, and version-controlled, and collaborate with the Cloud Security Engineer to meet PDPA/GDPR and internal standards.

5. Collaboration & Enablement Act as the embedded platform engineer — translating model and pipeline requirements into production-grade infrastructure decisions. Reduce time-to-production via self-service deployment templates and runbooks, and train engineers on deployment best practice and production readiness.

Qualifications Bachelor's degree in Computer Science, Software Engineering, or related field. 5+ years of DevOps/MLOps engineering experience; at least 2 years supporting ML or data science workloads in production. Deep proficiency in IaC (Terraform, AWS CDK, CloudFormation) and CI/CD platforms (GitHub Actions, Jenkins, AWS CodePipeline). Hands-on experience with ML platforms (SageMaker Pipelines, MLflow, Kubeflow) and containerisation/orchestration (Docker, Kubernetes/EKS, ECS). Proficiency in Python and Bash, and experience with monitoring tools (CloudWatch, Prometheus, Grafana, Datadog). AWS DevOps Engineer Professional or ML Specialty preferred; familiarity with data engineering stacks (Airflow, dbt, Spark) a strong plus.