Requirements

Expected technologies

Python

R

TensorFlow

PyTorch

Scikit-learn

SQL

NoSQL

Optional technologies

C++

AWS

Azure

Google Cloud

Docker

Kubernetes

Spark

Hadoop

Our requirements

• 3+ years of experience in machine learning engineering or a related field with a strong foundation in model development, deployment, and MLOps, and an interest in cutting-edge ML research.
• Bachelor’s or Master’s degree in computer science, Machine Learning, Statistics, or a related quantitative field, or equivalent experience.
• Proven experience as a Machine Learning Engineer with a focus on practical model development and deployment.
• Demonstrated ability to effectively design, implement, and evaluate machine learning solutions.
• A proactive attitude and a willingness to learn and contribute to advanced ML initiatives and research.
• Machine Learning Expertise: Proven ability to design, develop, and deploy machine learning models for various applications, with a focus on predictive analytics.
• Programming Languages: Strong proficiency in programming languages commonly used in ML (e.g., Python, R). Experience with C++ is a plus.
• ML Frameworks: Experience with popular machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Data Handling: Strong understanding of data manipulation and analysis techniques, including SQL and NoSQL databases.
• Analytical and Problem-Solving Skills: Ability to analyze complex problems, identify appropriate ML solutions, and troubleshoot model-related issues effectively.
• English Proficiency: Ability to read and write technical documentation and communicate effectively with colleagues in English.
• Interest in MLOps: A strong desire to learn and contribute to MLOps practices and infrastructure.

Optional

• Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) for ML deployment.
• Familiarity with containerization technologies (e.g., Docker, Kubernetes).
• Experience with big data technologies (e.g., Spark, Hadoop).
• Familiarity with Agile development methodologies (e.g., Scrum, Kanban).
• Domain Knowledge: Familiarity with industrial automation and/or predictive maintenance applications.
• Polish/Russian language skills.

Your responsibilities

• ML Model Development: Design, develop, and implement machine learning models based on requirements and real-world data, focusing on predictive maintenance and anomaly detection.
• Data Preprocessing and Feature Engineering: Perform thorough data collection, cleaning, transformation, and feature engineering to prepare datasets for model training and evaluation.
• Model Training and Evaluation: Train and optimize machine learning models, utilizing various algorithms and frameworks. Evaluate model performance using appropriate metrics and techniques.
• Model Deployment and Integration: Deploy ML models into production environments and ensure seamless integration with existing systems and applications.
• Model Monitoring and Maintenance: Implement monitoring strategies for deployed models to track performance, detect drift, and ensure ongoing reliability. Maintain and update models as needed.
• Algorithm Research and Selection: Stay up-to-date with the latest advancements in machine learning research and evaluate new algorithms and techniques for potential application within DFS solutions.
• Collaboration: Work closely with data scientists, software developers, product managers, and other stakeholders to understand requirements, provide technical insights, and ensure the successful delivery of ML-powered products.
• MLOps Involvement: Collaborate with MLOps engineers to streamline the ML lifecycle, including continuous integration, continuous delivery, and automated testing of ML models.
• Performance Optimization: Identify and implement optimizations for ML models and pipelines to improve efficiency, scalability, and resource utilization.
• Knowledge Sharing: Stay up to date with the latest ML methodologies, tools, and best practices, and share knowledge with the team.