Work on end-to-end ML Lifecycle from acquiring data, data cleaning, model building and deployment of models
Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
Verifying data quality, and/or ensuring it via data cleaning
Experience in building Machine Learning and Deep Learning models either with predictive algorithms, Timeseries, NLP or Computer Vision and deployment of the same
Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
Finding available datasets online that could be used for training and data augmentation pipelines
Defining validation strategies, defining preprocessing or feature engineering to be done on a given dataset
Training models and tuning their hyperparameters
Analyzing the errors of the model and designing strategies to overcome them
Deploying models to production
Ensure code paths are unit tested, defect free and integration tested
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 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:
2 4 years of experience in Data Science and 1-2 years as ML Engineer
Hands-on experience of 2+ years in writing object-oriented code using python
Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture.
In-depth knowledge of mathematics, statistics, and algorithms
Experience working with machine learning frameworks like Tensorflow, Caffe, etc.
Understanding of Data Structures, Data Systems and software architecture
Experience in using frameworks 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.)
