Luxoft company
🔔 Are you already in Poland? If YES this is the project for YOU!
Our benefits:
👩⚕️ Private Medical Care in Luxmed and Life Insurance
🏋️♀️ Multisport Card
👨👧👦 Paid referrals
📚 Self-learning libraries
🛫 Relocation package for seniors and assistance during all process...and MORE!
👉 Location: Remote in Poland
Project Description:
We are leveraging machine learning to drive innovation and enhance our products/services. As a Machine Learning Engineer, you will play a pivotal role in developing and deploying cutting-edge machine learning solutions to solve complex business problems and create value for our customers.
Responsibilities:
Develop and implement machine learning models and algorithms for various business applications, utilizing programming languages such as Python, R, or others as necessary.
Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions leveraging machine learning techniques.
Design and conduct experiments to evaluate the performance of machine learning models, iterate on model designs, and optimize for accuracy, scalability, and efficiency.
Clean, preprocess, and analyze large datasets to extract meaningful insights and features for model training and validation.
Mandatory Skills Description:
7+ years of hands-on professional MLOps engineering experience including understanding of container technology, Restful API and other various model deployment techniques and model performance measurement in production.
• Expertise in MLOps, Feature Store, Model Serving design, implementation, and operations.
• Expert proficiency in programming and querying languages including Python and SQL
• Proficiency in big data processing, including Apache Spark and Databricks
• Expertise in PySpark
• Proficiency in technologies like MLFlow, Azure ML services etc.
• Agile methodologies
Nice-to-Have Skills Description:
• Natural sense of urgency, teamwork, and collaboration reflected in daily work ethic
• Master's degree in computer science or related quantitative field of study
