ML​/Ops Engineer strong Azure cloud

ML/Ops Engineer with strong Azure cloud experience

Remote Position

Duration: 12+ months

Client is seeking a highly skilled ML/Ops Engineer with strong Azure cloud experience to design, build and operate our enterprise Machine Learning Operations (MLOps) platform. In this role you will work closely with data scientists, ML engineers, Dev Ops teams and business stakeholders to ensure that ML models are delivered into production in a scalable, secure and cost‑efficient way. You will help define best practices and drive automation, monitoring and governance for the ML lifecycle.

• Architect, build and maintain the ML/Ops infrastructure on Microsoft Azure: including services such as Azure Machine Learning, Azure Databricks, Azure Kubernetes Service (AKS), Azure Functions, Azure Storage, etc.
• Design and implement end‑to‑end CI/CD pipelines (data ingestion → feature engineering → model training → deployment) using Azure Dev Ops (or equivalent) to streamline model delivery.
• Develop Infrastructure as Code (IaC) using Terraform, ARM templates or Bicep to provision and manage cloud resources for ML workloads.
• Containerize ML models using Docker, orchestrate via Kubernetes/AKS and deploy as microservices or batch jobs in production.
• Monitor production models: track model performance, drift, latency, throughput, costs; use services like Azure Monitor, Azure Log Analytics for observability.
• Work with data scientists and engineers to enable feature stores, experiment tracking (e.g., MLflow) and model versioning.
• Optimize compute and storage usage: cost‑manage cloud resources, right‑size clusters, auto‑scale jobs.
• Ensure platform security, compliance and governance: implement access controls, encryption (at rest/in transit), logging, auditability and data privacy standards.
• Collaborate with cross‑functional teams: enable reuse of ML platform capabilities across business units, provide documentation, training and support.
• Continuously evaluate and adopt new Azure/ML technologies and best practices to improve platform performance, scalability and maintainability.

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience).
• 5+ years (or adjust to seniority) of experience in a production ML engineering, MLOps, or cloud engineering role.
• Proven experience with Azure cloud platform:
  Azure Machine Learning, Azure Databricks, AKS, Azure Storage/Blob, Azure Functions, Azure Dev Ops pipelines.
• Strong programming skills in Python and familiarity with ML frameworks (Tensor Flow, PyTorch, scikit‑learn).
• Solid experience with containerization (Docker) and orchestration (Kubernetes/AKS).
• Hands‑on in CI/CD tooling, version control (Git), infrastructure as code (Terraform/ARM/Bicep).
• Experience deploying and operating ML models in production environments, including model monitoring, drift detection, logging and instrumentation.
• Good data engineering skills: working with SQL/noSQL, ETL/ELT pipelines, large data volumes, feature engineering.
• Excellent problem‑solving skills, ability to troubleshoot production issues in distributed/cloud systems.
• Strong communication and collaboration skills: able to partner with data scientists, software/Dev Ops engineers and business stakeholders.

• Experience with LLMs (large‑language models), computer vision or multimodal ML use cases.
• Familiarity with other public clouds (AWS/GCP) or hybrid/multi‑cloud environments.
• Experience with streaming/event‑driven architectures (Kafka, Azure Event Grid, Service Bus).
• Prior exposure to healthcare, supply chain or pharmaceutical domain.
• Certifications such as Microsoft Certified:
  Azure Solutions Architect, Azure Dev Ops Engineer Expert or Azure Data Scientist Associate.
• Experience with ML observability tools (e.g., Evidently, Prometheus/Grafana) or service mesh for microservices (Istio).

Metasys Technologies is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identify, national origin, veteran or disability status.