(Senior) Engineering Manager, Machine Learning

(Senior) Engineering Manager, Machine Learning

We are looking for a (Senior) Engineering Manager to lead a Helsinki-based AI/ML engineering team. Depending on experience, this role may be filled at Engineering Manager or a Senior Engineering Manager level.

About the 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.

We're looking for someone who:

• Wants to exceed the expectations for the performance and impact of the team but is also keen to maintain our inclusive and supportive, healthy work environment.
• Brings substantial engineering management experience across different team shapes and contexts, and can draw on that breadth to design the right structure and ways of working for a large, technically complex team.
• Understands the day-to-day work of a software developer and a machine learning engineer through a technical background or other extensive experience working with a development team.
• Has experience leading or working alongside ML engineers on production-grade ML systems.
• Comfortable with experimentation-driven work and leading delivery in environments with technical uncertainty and iterative outcomes.
• Thinks critically about how to deliver iteratively and incrementally, with a genuine understanding of agile principles rather than ceremony — someone who shapes process around the team's real needs and uses data to guide what to build next and how.
• Excels in communication and collaboration with other engineering teams, product management and business stakeholders in English.
• Has a good understanding of modern cloud technologies and SaaS development.

Deep expertise in our specific stack is not a prerequisite — we care more about your ability to lead and learn. That said, familiarity with some of the following will help you ramp up faster:

• Python-based ML ecosystem: PyTorch, experiment tracking (MLflow, W&B), feature pipelines, and model serving
• MLOps practices: model versioning, automated training pipelines, CI/CD for ML, monitoring and drift detection
• LLMs and NLP: RAG architectures, fine-tuning, evaluation frameworks, prompt engineering, API-based integration
• Production observability: Grafana, Prometheus, logging and alerting for ML systems
• Microsoft technologies: .NET, C#, Azure

What it's like working at Sievo:

• You’ll get to build the leading procuremen