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MLOps Engineer
MLOps Engineer
MLOps Engineer to design, deploy, and validate machine learning models using Azure cloud services, Python frameworks, and automated DevOps pipelines.
About the role
Key Responsibilities
- Develop and deploy machine learning applications for prediction, recommendation, text analytics, computer vision, bots, and document intelligence.
- Design and maintain infrastructure for dataset ingestion, normalization, and combination to derive actionable insights.
- Deploy and validate machine learning models using frameworks like TensorFlow, PyTorch, Keras, Spacy, and scikit-learn.
- Utilize Azure ML Studio and Azure Kubernetes Service for scalable model inferencing and deployment.
- Automate deployments using Terraform and DevOps principles, ensuring high-quality model validation and quality control.
- Collaborate with cross-functional teams to ensure client satisfaction and project success.
Requirements
- 8+ years of experience in MLOps and cloud-based machine learning deployments.
- Hands-on expertise with Azure ML Studio, Azure Kubernetes Service, and Terraform for infrastructure as code.
- Proficiency in Python and open-source ML frameworks (TensorFlow, PyTorch, Keras, Spacy, scikit-learn).
- Experience with model inferencing, validation, and deployment pipelines.
- Strong understanding of DevOps principles and automated deployment workflows.
Skills
mlopsazure ml studioazure kubernetes serviceterraformpythontensorflowpytorchkerasspacydjangofastapiargobentoml