ML Integrations Engineer

OneRail is a technology company innovating solutions for multi-modal delivery. They have built and continue to develop a delivery orchestration platform that connects shippers to a courier ecosystem to automate and control the delivery supply chain. Their OmniPoint SaaS platform uses proprietary algorithms and artificial intelligence to power efficient same-day delivery programs for companies like Lowe’s and Pepsi.

• 3+ years of professional full-stack development with ML integration experience.
• Hands-on experience serving ML models in production (model serialization, serving frameworks, inference optimization).
• Strong proficiency in TypeScript and Python.
• Experience with microservices architecture, message brokers (e.g., Kafka), and async frameworks.
• Solid React knowledge including hooks, context, and state management.
• Deep understanding of REST principles, OpenAPI/Swagger, and API versioning/backward compatibility.
• Experience with Docker and orchestration.
• Familiarity with CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI) and automated testing (unit, integration, e2e).
• Strong communication skills for technical and non-technical stakeholders.
• Excellent problem-solving skills and scalable system design expertise.
• Advanced English proficiency (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/AI features.
• Define and evolve RESTful APIs with OpenAPI/Swagger for low-latency, high-throughput ML inference.
• Version, document, and maintain API contracts for internal and external consumers.
• Build microservices in 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 features.
• Ensure seamless data flow from backend to frontend, handling streaming or polling.
• Containerize ML services using Docker and orchestrate deployments.
• Collaborate with cross-functional teams (data science, product, QA) to translate ML requirements into production features.
• Champion code quality, API versioning strategies, and thorough documentation.

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