Own the end-to-end deployment of machine learning models and build reliable services for diagnostics in industrial equipment, enhancing operational efficiency.
Data Science at TRACTIAN
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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