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.