(Senior) Engineering Manager, Machine Learning - Sievo Oy - Helsinki, Suomi

We are looking for a (Senior) Engineering Manager to lead a Helsinki-based AI/ML engineering team. The team builds the intelligence capabilities powering Sievo's procurement platform, from spend classification and data harmonization to applied AI products. The team has a critical role in turning Sievo's unique domain data and rich cross-customer datasets into a durable competitive advantage and new business opportunities through machine learning, while enabling product teams to integrate AI-driven features into their solutions with confidence. As an Engineering Manager, you will be accountable for delivery, technology, and people in the team of around 10 ML and software engineers currently organized in two pods. You will guide your team to produce high-impact outcomes in an environment where experimentation, iteration, and technical uncertainty are the norm rather than the exception. Partnering with your Product Manager and a Staff MLE, you ensure the team is focused on the highest-leverage ML investments and that models move reliably from prototype to production. Organizing the team effectively to meet evolving demands, you drive cross-team collaboration to keep shared initiatives moving with clarity and momentum. Supporting and motivating your engineers, you help them pursue a growing level of performance and enable career growth. While you share overall responsibility for the team's technology, you drive towards empowering your engineers to make the right technical choices and fostering a culture of rigorous evaluation and continuous improvement. Key responsibilities: People management responsibilities, including inspiring, empowering, and coaching the team members to achieve a high level of technical expertise, productivity, and efficiency. Ensuring that the team understands the business domain and defines the solutions and technical architectures that solve the correct problems. Ensuring that the team's services run smoothly in production and meet the expected level of maintainability, security, usability, reliability, and other quality requirements. Defining and continuously improving how the team is organized, how work flows through it, and how agile practices and day-to-day routines support sustainable, high-quality delivery. Driving cross-team collaboration and ensuring crisp execution on shared initiatives, with clear alignment among stakeholders on dependencies, timelines, and progress.