MLOps Engineer
Location:
Orlando, FL (Onsite)
Type:
Long-Term Contract
About the Role:
We are seeking a highly skilled and experienced MLOps Engineer to join our dynamic team in Orlando, FL. In this role, you will be responsible for designing, implementing, and maintaining robust MLOps solutions on the Azure cloud platform. You will collaborate closely with data scientists, data engineers, and architects to streamline the machine learning lifecycle, from model development to deployment and monitoring.
Experience Required:
- Overall 10 years out of 5-8 years relevant experience in MLOps.
- Azure + Terraform IAC, ML FLow and ML re-training pipelines
- can support the team with deploying LLMs, ML inference APIs in Azure or Databricks
- Can deploy monitoring and full set of observability set.
- Deep quantitative/programming background with a degree (Bachelor’s) in a highly analytical discipline, like Statistics, Economics, Computer Science, Mathematics, Operations Research, etc
Responsibilities:
- Design and implement cloud solutions, build MLOps on Azure cloud
- Build CI/CD pipelines orchestration by Azure Devops or similar tools
- Data science model review, code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality
- Data science models testing, validation and tests automation
- Communicate with a team of data scientists, data engineers and architect, document the processes
Requirements:
- Overall 10 years out of 5-8 years of experience in managing machine learning projects end-to-end, with the last 18 months focused on MLOps.
- Monitoring Build & Production systems using automated monitoring and alarm tools
- Knowledge of machine learning frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn
- Knowledge on building pipelines using synapse, databricks end to end.
- Experience in API integration and data feeds with social analytics(Facebook, Instagram, Twitter).
- Experience with MLOps tools such as ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC)
- Experience in supporting model builds and model deployment for IDE-based models and autoML tools, experiment tracking, model management, version tracking & model training (Dataiku, Datarobot, Kubeflow, MLflow, neptune.ai) will be an addon, model hyperparameter optimization, model evaluation, and explainability (SHAP, Tensorboard)
- Experience in Databricks, Azure DataLake Gen2 and Unity Catalog
- Ability to understand tools used by data scientist and experience with software development and test automation
