Integration Engineer - Machine Learning

OneRail Poland Sp. z o.o. works in logistics and technology domains, focusing on integrating machine learning models into production systems.

• 3+ years of professional full-stack development experience integrating machine learning into production.
• Hands-on experience serving machine learning models in production including model serialization, serving frameworks, and inference optimizations.
• Strong proficiency in TypeScript and Python.
• Experience with microservices architecture, message brokers such as Kafka, and asynchronous frameworks.
• Solid experience with React (hooks, context, state management).
• Deep understanding of REST principles, OpenAPI/Swagger, and API versioning/backward compatibility.
• Experience in Docker and container orchestration.
• Experience with CI/CD pipelines such as GitHub Actions, Jenkins, GitLab CI and automated testing (unit, integration, e2e).
• Excellent communication skills, able to translate technical concepts to non-technical stakeholders.
• Strong problem-solving skills with attention to scalable system design.
• Advanced proficiency in English (B2+).

• Work with ML pipeline engineers to deploy and maintain ML models (e.g., PyTorch, scikit-learn) as robust service endpoints.
• Collaborate with data scientists and product teams to productionize prototypes and extend product ML and AI features.
• Define and evolve RESTful APIs with OpenAPI/Swagger, ensuring support for low-latency, high-throughput ML inference.
• Version, document, and maintain API contracts for internal and external consumers.
• Build microservices (Python—FastAPI/Flask or Node.js) encapsulating ML logic, data pre/post-processing, and business rules.
• Optimize service performance, scalability, and reliability (caching, async processing, batching).
• Implement and modify React components and pages to expose ML-driven functionality.
• Ensure seamless data flow from back end to front end, handling streaming or polling as needed.
• Containerize ML services using Docker and orchestrate deployments.
• Work closely with cross-functional teams (data science, product, QA) to translate ML requirements into production features.
• Champion code quality, API versioning strategies, and thorough documentation.