Job Description

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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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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