MLOps Engineer
Core
Design, build, and support cloud-native infrastructure and automation systems to enable reliable data workflows, ML model deployment, and observability for Data Science and Software Engineering teams.
Role type
Senior MLOps Engineer (Infrastructure & Automation)
Builds
Scalable, cloud-native MLOps platforms, automated CI/CD pipelines, and containerized ML workloads.
Domain
Construction industry, Cloud Infrastructure, Machine Learning Operations
Deliverable
infrastructure
Required skills
CI/CD automation, Infrastructure as Code (Terraform, Bicep), Container orchestration (Kubernetes/AKS), Observability tooling (Datadog, Azure Monitor, Grafana), Production ML model deployment, Model explainability (SHAP, LIME), Cloud cost management
Preferred skills
Azure ecosystem (AKS, ACR, ARM, App Service, Azure ML), Semantic search/RAG pipelines, Workflow orchestration (Airflow, Argo, Prefect), Snowflake
Technologies
Azure, Kubernetes, AKS, Terraform, Bicep, Datadog, Azure Monitor, Grafana, TensorFlow, PyTorch, Scikit-learn, Snowflake, Apache Airflow, Argo Workflow, Prefect
Responsibilities
Lead hands-on implementation of automation-first DevOps and MLOps practices; Design and manage intelligent DataOps pipelines with automated data quality monitoring; Standardize observability practices across AI/ML teams; Design and deploy containerized ML workloads; Extend CI/CD pipelines for automated infrastructure changes; Implement AI-driven data validation and metadata management; Establish governance frameworks for AI systems including bias detection and explainability.
Seniority
Senior, hands-on IC
