Machine Learning Engineer

At Rohlik Group, we are not just delivering groceries; we are engineering the future of operational efficiency. Our vision is to build a truly autonomous operational backbone, where intelligent systems optimize the physical heart of our business—our warehouses and last-mile delivery networks. We are seeking a forward-thinking Machine Learning Engineer to build the predictive engines that will power this transformation.

This is not a role for someone who just wants to build models in a notebook. This is an opportunity for a systems builder who is passionate about deploying production-grade ML to solve complex, physical-world problems. You will have the autonomy to design, build, and own the critical ML services that will redefine efficiency, reduce waste, and create a seamless customer experience from the warehouse shelf to their front door.

As a Machine Learning Engineer in the Operations Excellence Tribe, you will have end-to-end ownership of the ML services that drive our daily operations. Your responsibilities will span the full lifecycle, from system architecture to operational excellence.

• Architect & Deploy Operational Models

• Design, build, and fine-tune the predictive engines that power our operational core, including advanced time-series models for demand forecasting, warehouse planning, and delivery time predictions.

• Leverage both classic machine learning and advanced methods to create specialized predictive systems that are optimized for performance, cost, and accuracy
• Use AI and machine learning to automate decisions and minimize manual intervention in daily warehouse and last-mile operations.

• Master End-to-End MLOps

• Own the full MLOps lifecycle for our operational ML services, including building robust CI/CD pipelines for automated testing and reliable model deployment using tools like ZenML and Google Cloud Platform (GCP).

• Implement comprehensive observability and monitoring for your models, tracking performance, data drift, and the real-world business impact of your systems.
• Ensure your deployed models meet the highest standards of quality and reliability through rigorous evaluation and continuous improvement.

• Drive Operational Excellence & Innovation

• Partner directly with warehouse managers, logistics planners, and last-mile teams to identify high-impact use cases for ML and AI.

• Translate complex algorithms and data insights into pragmatic, actionable improvements that teams can implement, valuing a "done" solution that drives impact over a "perfect" one.
• Proactively explore and experiment with new ML/AI trends to continuously enhance our operational capabilities.

We are looking for a pragmatic and product-focused engineer who combines deep technical expertise with a passion for seeing their work come to life in the real world.

• Foundational Experience

• A strong background with proven proficiency in Python and SQL.

• Experience building and deploying applications on a major cloud platform, with a preference for Google Cloud Platform (GCP).
• A solid understanding of core AI and machine learning fundamentals.

• Core ML Expertise (Must-Have)

• Proven, hands-on experience building and deploying time-series forecasting models for real-world applications.

• Deep expertise in applying general ML methods to operational challenges.
• Experience with combining traditional ML methods with LLMs is highly desirable. A degree in a relevant field like Mathematical Statistics or Computer Science is a plus.

• Production & MLOps Skills (Good-to-Have)

• Experience deploying, monitoring, and maintaining ML systems in a production environment.

• Familiarity with containerization technologies (Docker, Kubernetes) and CI/CD principles for machine learning.
• Knowledge of MLOps principles and tools for versioning, observability, and evaluation.

• Strategic & Collaborative Skills

• A pragmatic, product-oriented mindset with a focus on delivering practical solutions that drive business value.

• Excellent communication skills, with the ability to articulate complex technical concepts to operational and other non-technical stakeholders.

• Tangible Impact: You won't just build models; you'll see them in action on the warehouse floor and in our delivery routes. Your work will directly translate into measurable business outcomes.
• End-to-End Ownership: You will have the autonomy and responsibility to own critical ML products from conception to decommission, directly influencing our company's operational strategy.
• Cutting-Edge Problems: You will tackle unique and challenging problems at the intersection of AI and physical logistics, from building models that guide warehouse inventory flow to developing predictive maintenance schedules for our delivery fleet.