MLOps Engineer

Job Title: MLOps Engineer
Location: Portland, OR (5 days Onsite), they may ask for F2F client interview.

Job Description

We are seeking an experienced MLOps Engineer to join our team onsite in Portland, OR. The ideal candidate will be responsible for designing, deploying, automating, and maintaining machine learning pipelines and infrastructure. You will work closely with data scientists, software engineers, and cloud teams to operationalize ML models and ensure scalable, secure, and reliable AI/ML solutions.

Key Responsibilities

• Design, build, and maintain end-to-end MLOps pipelines for model training, testing, deployment, and monitoring.
• Automate ML workflows using CI/CD best practices.
• Deploy and manage machine learning models in production environments.
• Develop scalable data and model pipelines on cloud platforms.
• Monitor model performance, data drift, and system health.
• Collaborate with data scientists to productionize ML models.
• Implement model versioning, experiment tracking, and artifact management.
• Optimize infrastructure for performance, scalability, and cost efficiency.
• Ensure security, governance, and compliance for ML platforms.
• Troubleshoot production issues and improve operational reliability.

Required Skills

• 5+ years of experience in DevOps, Data Engineering, or MLOps.
• Strong experience with Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
• Experience with containerization technologies like Docker and Kubernetes.
• Strong knowledge of CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
• Experience with cloud platforms (AWS, Azure, or Google Cloud Platform).
• Experience with Infrastructure as Code tools such as Terraform or CloudFormation.
• Knowledge of model monitoring, logging, and observability tools.
• Strong understanding of Git version control and software development best practices.
• Experience with Linux environments and shell scripting.

Preferred Qualifications

• Experience with Generative AI, LLM deployment, or RAG-based applications.
• Familiarity with Apache Airflow, Kafka, or Spark.
• Knowledge of feature stores and model registries.