Machine Learning Engineer

Imagine a role not just built around your strengths—but one that invites your becoming. Where deep Python fluency, full-spectrum IT wisdom, and a hunger for real-world change are not endpoints, but entryways. You’ll join a tight-knit squad of 5 (within a growing chapter of 13), where your end-to-end craft—designing resilient ML pipelines, debugging with the calm of experience, deploying models that truly serve—shapes both product and person. This isn’t about writing models in isolation. It’s about owning systems, solving real problems, and growing into something larger than your title. With tools like Keboola and GCP, you’ll build forecasts that matter—cutting waste, automating judgment, keeping shelves filled with meaning. Move fast. Think deep. And find, in the midst of it all, that who you are becoming is just as important as what you build.

What will be your key responsibilities:

1. Forecast & Optimize

Develop and fine-tune forecasting models that drive warehouse planning, demand prediction, and supply chain efficiency.

2. Automate Decisions

Use AI and machine learning methods—along with LLM APIs—to minimize manual intervention in daily operations.

3. Integrate Seamlessly

Deploy, scale, and maintain ML pipelines using Keboola or GCP, ensuring high availability and performance.

4. Incorporate MLOps & CI/CD

Implement best practices for continuous integration, continuous delivery, and reliable model deployment under tight deadlines.

5. Collaborate & Innovate

Partner with various teams to identify high-impact use cases for ML and AI, especially in supply chain, personalization, pricing, and planning.

6. Balance Speed & Vision

Deliver quick, pragmatic solutions while considering broader strategic goals. Rapid iterations are crucial, but so is maintaining a forward-looking approach.

What experience should you have:

🐍 8+ Years of Python Mastery

• Nearly a decade writing clean, production-grade Python; fluent in ML tools (pandas, scikit-learn, XGBoost); builds/debugs complex systems end-to-end. Time series exposure is a plus.

🛠 Full-Spectrum IT Experience

•  Full lifecycle engineer—design to support—across cloud, CI/CD, ops, and scaling. Comfortable with debugging, log diving, and adopting new tech pragmatically. GCP certification is a plus.

🧪 Technical or Scientific Leadership

•  Led ML or research projects; drives alignment, planning, and execution.

💡 Strategic + Pragmatic Thinking

•  Can prototype in 4 hours or think in 3-year arcs. Balances speed and structure; connects tactical with strategic.

🏃 Proven in High-Stakes, Fast-Moving Environments

•  Delivered under pressure in dynamic orgs—whether big tech, consulting, or fast-scaling startups. Turns ambiguity into clarity. Amazon/McKinsey/EY experience is a plus.

🧠 Dual Academic Background or π-Shaped Expertise

•  Multiple degrees or hybrid strengths (e.g., stats + product); blends technical depth with strategic range.

🌟 Presence in Action: From Stage to Strategy

•  Presented to execs, led workshops, or spoken at events; clear, confident communicator. Exposure to scout leadership, teaching at any level, (improvisational) theater, hackathons, volunteering, survival experiences, music ensembles, or debate clubs is a plus.

🗣 Fluent in English (and human)

•  Aligns teams through clarity, empathy, and calm. Handles tension and conflict constructively.

🌍 Team Enabler & Cultural Contributor

•  Brings more than code—trust-building, adaptability, and feedback-driven growth. Seeks critique, values team maturity, and contributes to culture.

What do you get in return:

• Direct impact on company results🌟

• Innovation over trend-following💡

• Dog-friendly office🐶

• Unlimited holidays🏝

• Hybrid working model, 3 days in the office and 2 days at your home office + flexible working hours ⌚

• Rohlik premium for free, 10% discount🥐

• Free kindergarten services for employees👼

• Inspiring meetings, lots of food, a great team, and many more!🌈