About the job Sr. MLOps Engineer
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| Skills | Year of experience | Remarks | Weightage |
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| MLOps & Python | 3+ | Mandatory | 60% |
| Cloud | 3+ | Mandatory | 20% |
| Machine Learning | 2+ | Good to have | 10% |
| DevOps | | Good to have | 10% |
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Job Description
Total Exp in years - 4 7
Responsibilities:
- Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality.
- Design and implement cloud solutions, build MLOps on cloud (preferably Azure)
- Work with workflow orchestration tools like Kubeflow, Airflow, Argo or similar tools
- Data science models testing, validation and tests automation.
- Communicate with a team of data scientists, data engineers and architect, document the processes.
Mandatory Skills:
- 4 7 years of experience in MLOps and Data engineering
- Rich hands-on experience of 3+ years in writing object-oriented code using python
- Min 3 years of MLOps experience (Including model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning, Automated pipelines)
- Understanding of Data Structures, Data Systems and software architecture
- Experience in using MLOps frameworks like Kubeflow, MLFlow, Airflow Pipelines for building, deploying, and managing multi-step ML workflows based on Docker containers and Kubernetes.
- Experience with Azure cloud services, Cosmos DB, Streaming Analytics, IoT messaging capacity, Azure functions, Azure compute environments, etc.
- Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)
- Strong DevOps mentality: Knowledge of making a complicated pipeline simple and easy to maintain, with proven experience of Terraform/Spark