Senior Machine Learning Engineer

Stack

Responsibilities

• ▹Own ML projects end to end: plan the roadmap, frame the problem, build pipelines, take solutions to production
• ▹Translate business goals into ML solutions and explain results, limitations and uncertainty to business stakeholders
• ▹Make technical decisions, contribute significantly to implementation and mentor engineers through code review and design discussion
• ▹Deliver forecasting, classification and anomaly detection on time series from industrial IoT sensors
• ▹Work with the realities of sensor data: gaps, drift, scarce labels and an evolving device population
• ▹Run what you build: monitoring, drift detection and retraining, and shape the data pipelines your models depend on
• ▹Use agentic coding tools (Claude Code, Copilot, Cursor and similar) in daily delivery and hold AI-generated code to the same bar
• ▹Structure repositories, tests and documentation so both people and agents can work in them effectively
• ▹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
• ▹Shape best practices across data engineering, machine learning and MLOps

Requirements

• ▹Bachelor's degree in Computer Science, Data Science, Engineering or a related quantitative field, or equivalent combination of education and experience
• ▹5+ years in machine learning engineering or a closely related software engineering role with hands-on production deployment (6-8 preferred)
• ▹Hands-on experience delivering production-level, cloud-native machine learning solutions
• ▹Strong Python and daily engineering habits: git, code review, linters, unit tests, CI/CD
• ▹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, retraining and MLOps practices
• ▹Fluency with agentic coding tools
• ▹Fluent in written and spoken English

Nice to have

• ▹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
• ▹Domain experience in industrial, energy or IoT settings

Soft skills

What we offer

• ▹Hybrid role based in the Porto office; most work can be done remotely with occasional in-office time for team collaboration

About the company

A leading company in Industrial IoT (IIoT) solutions, supporting safe, efficient and reliable operations across industries worldwide.