The Data Science team at TRACTIAN focuses on extracting valuable insights from vast amounts of industrial data. Using advanced statistical methods, algorithms, and data visualization techniques, this team transforms raw data into actionable intelligence that drives decision-making across engineering, product development, and operational strategies. The team constantly works on optimizing prediction models, identifying trends, and providing data-driven solutions that directly enhance the company’s operational efficiency and the quality of its products.
What you’ll do
We’re hiring a Mid-Level Machine Learning Engineer to bridge the gap between data science and production systems. You’ll own end-to-end deployment of machine learning models, work with real-time sensor data, and build reliable services that power diagnostics for industrial equipment. This is a hands-on role with real impact, ideal for engineers who want to grow their systems design and ML Ops skills.
Responsibilities
- Deploy and maintain ML models from the data science team
- Design and implement APIs and real-time inference services
- Work with large-scale time-series datasets from vibration and sensor systems
- Improve the performance and reliability of model serving pipelines
- Monitor system health and implement logging, alerting, and fallback mechanisms
- Contribute to architectural decisions and collaborate across teams
Requirements
- 2–4 years of experience in software or machine learning engineering
- Bachelor’s degree in Computer Science, Engineering, or related technical field
- Solid background in math, statistics, and machine learning concepts
- Strong Python skills and experience with ML libraries like scikit-learn or PyTorch
- Experience deploying models in production environments
- Familiarity with event-driven platforms and message queues (e.g., Kafka, Redis Streams)
- Comfort working with streaming or time-series data
Preferred Qualifications
- Experience with containerization (Docker) and cloud deployment
- Exposure to real-time or low-latency systems
- Interest in optimization of inference latency and resource usage
Technical Skills
- Programming: Python, Golang
- ML Libraries: scikit-learn, PyTorch, TensorFlow
- Backend: FastAPI, Flask
- Infrastructure: Kafka, Redis, PostgreSQL, Docker
- ML Ops: Model serving, monitoring, CI/CD pipelines
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