Job description

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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

Sr. MLOps Engineer

Role overview