Senior Machine Learning Engineer

Description

Summary: Own machine learning solutions end-to-end, translating business goals into ML solutions and delivering forecasting, classification, and anomaly detection. Highlights: 1. Make a measurable and mission-critical impact in Industrial IoT solutions 2. Collaborate for success, leading ML projects from planning to production 3. Engineer with AI assistance, utilizing agentic coding tools daily **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.