What are the responsibilities and job description for the MLOps Engineer position at The Avian Consulting LLC?
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.