Senior Machine Learning Engineer

Make a measurable and mission-critical impact. Bring your unique talents and experience to a leading company in Industrial IoT ( IIoT ) solutions. Grow your passion into a rewarding profession by joining a dynamic and expanding organization. You’ll play a vital role that supports your success and helps drive safe, efficient, and reliable operations across industries worldwide. Where you’ll work: This is a hybrid role based out of our Porto office. In practice, most of your work can be done remotely, with occasional in-office time in Porto for team collaboration — a flexibility our engineers consistently tell us they value. Job Duties and Responsibilities: You will own machine learning solutions end to end — from framing the business problem to running models reliably in production — built on real-time telemetry from industrial IoT sensors deployed around the world. Collaborate for success

• Own machine learning projects end to end: plan the roadmap, frame the problem, build the pipelines, and take solutions through to production.
• Translate business goals into ML solutions, and explain results, limitations and uncertainty to business stakeholders in terms they can act on.
• Make the technical decisions, contribute significantly to the implementation, and mentor other engineers through code review and design discussion. This is a hands-on role. Build ML-powered solutions
• Deliver forecasting, classification and anomaly detection on time series from industrial IoT sensors reporting in real time from sites across the globe.
• Work with the realities of sensor data: gaps, drift, scarce labels, and a device population that keeps evolving.
• Run what you build — monitoring, drift detection and retraining — and shape the data pipelines your models depend on. Engineer with AI assistance
• Use agentic coding tools — Claude Code, Copilot, Cursor and similar — as a normal part of daily delivery.
• Hold AI-generated code to the same bar as any other code. You are accountable for what you ship.
• Structure repositories, tests and documentation so both people and agents can work in them effectively, and share the patterns and guardrails that work so the team's baseline rises.
• Apply Anova's AI Handbook guidance on model risk and human-in-the-loop validation to any model whose output reaches a customer or drives an automated action. Advocate for quality Contribute to and continuously adapt best practices and Ways of Working across data engineering, machine learning and MLOps , so the team ships high-quality solutions that create real impact for our clients. Minimum Requirements -
• Bachelor's degree in Computer Science , Data Science, Engineering, or a related quantitative field or equivalent combination of education and experience
• 5+ years of experience in machine learning engineering or a closely related software engineering role, including hands-on production deployment (6–8 years preferred).
• Hands-on experience delivering production-level, cloud-native machine learning solutions.
• Strong Python and the engineering habits that go with it: git, code review, linters, unit tests and CI/CD pipelines are things you use daily.
• Strong understanding of feature engineering, ML algorithms, model training and evaluation.
• Solid experience across a modern ML stack: gradient boosting ( LightGBM , XGBoost ), scikit-learn, PyTorch , MLflow , and current time series tooling.
• Experience operating models in production: deployment, monitoring, drift detection and retraining, and a feel for the MLOps practices that make that sustainable.
• Fluency with agentic coding tools.
• Fluent in written and spoken English. Preferred Qualifications -
• Depth in the Azure Databricks platform: PySpark , MLflow , streaming pipelines.
• Experience implementing agentic workflows in production.
• Familiarity with MCP (Model Context Protocol) or similar patterns for exposing models as tools other agents can call directly.
• Domain experience in industrial, energy or IoT settings.