Informacje o stanowisku

We are looking for a highly-motivated Senior Machine Learning Engineer candidate that is able to drive scalable, production-grade machine learning pipelines. You will have a unique opportunity to deliver ML/AI solutions to a broad variety of our clients and participate in the development of our products and services. As far as corporations go, this is the most startup role in the Big4 environment that exists. Your potential superiors are really engaged, understand cutting-edge approaches, and can still challenge you to make this a creative environment.

Responsibilities:

• Working as a part of the Prague Data Science team and our clients global network of experts, you will be responsible for designing, developing, deploying, and maintaining scalable production-grade machine learning pipelines.
• You will need to deep dive into machine learning and deep learning code, data management and model versioning, training, tuning, and serving. All that packed into fully automated DevOps pipelines.
• Partner with our clients colleagues as well as clients to deliver production-ready AI/ML solutions.

Requirements:

• Technical background in IT, statistics, mathematics, or operational research.
• Strong experience in Artificial Intelligence, Machine Learning, and Big Data processing or distributed computation (Spark).
• 3+ years programming skills in Python (or other programming languages used for ML such as Java, R, Julia...) and knowledge of its best practices.
• Hands-on experience with container orchestration - Kubernetes, Docker, and corresponding Azure stack.
• Build and maintain tools and infrastructure for data processing and AI/ML development, model inference (REST API, GRPC, PubSub).
• Experience with machine learning platforms - Azure ML studio, Amazon SageMaker studio, or Dataiku.
• Knowledge of MLOps principles and experience with MLOps platforms such as Kubeflow, Airflow, ML Flow.
• You stick to automated CI/CD development and deployment processes using Azure DevOps, Gitlab, or similar tools.
• Experience with ML training/retraining, hyperparameter tuning, Model Registry, ML model performance measurement using ML Ops open-source frameworks.
• You have effective and professional verbal and written communication.
• Analytical and problem-solving skills with attention to detail.
• Willingness to learn new tools and design new solutions.

Additional requirements (desirable):

• Familiar with database tools and data manipulation techniques (SQL, OLAP, ETL).
• Knowledge of Microsoft Azure data stack (Azure Data Factory, Azure Blob Storage, Azure Databricks).
• Experience in effective visualization (Tableau, PowerBI, Plotly, Dash).
• Interest in behavioral economics and/or data science.
• Kaggle or Github profile.

If interested, please get in touch via contact details provided or click “Apply” to forward an up-to-date copy of your CV.

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