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ABOUT THE OPPORTUNITY
This is a rare role that sits at the real intersection of data engineering and machine learning — not a data engineer occasionally touching models, and not a data scientist handed a Jupyter notebook and told to "put it in production." You'll be the engineer who owns the full journey: from raw data through feature engineering, model training, deployment, and live monitoring.
The company behind this role is one of the world's leading job platforms, operating 20+ brands across 30+ countries — and this expansion into Portugal is being built from the ground up, in close partnership with a local tech consultancy deeply embedded in the Portuguese engineering ecosystem. If you want to do serious ML infrastructure work at global scale, from Portugal, fully remote — this is the opportunity.
PROJECT & CONTEXT
You'll join a cross-functional ML engineering team building the pipelines, feature stores, and serving infrastructure that take models from experimentation into production. The work is technically broad — batch and real-time inference, CI/CD for models, feature platform design, and observability — and has direct product impact on how millions of job seekers and employers are matched globally.
You'll collaborate daily with data scientists, product engineers, and platform engineers in an environment where ML is a core capability, not a side project. Full remote, consulting engagement, based in Portugal.
WHAT WE'RE LOOKING FOR
3–8 years of combined experience in data engineering and ML model lifecycle work (Mid: 3–5 yrs / Senior: 5–8 yrs)
Advanced, production-grade Python skills — model training, serving, and automation
Hands-on experience with at least one ML framework: scikit-learn, PyTorch, or TensorFlow
Experience with MLOps tooling: MLflow, Kubeflow, or Amazon SageMaker for experiment tracking and model serving
Solid understanding of the end-to-end ML lifecycle: data ingestion feature engineering training evaluation production
Experience designing and owning feature stores for both batch and real-time inference
Model deployment and REST or gRPC serving patterns in production environments
Hands-on with cloud ML services: Amazon SageMaker, GCP Vertex AI, or Azure ML
Data pipeline tooling: Apache Airflow, Apache Spark, or dbt
CI/CD for ML — model registry management, A/B testing frameworks, and production monitoring
Strong SQL and data wrangling skills at scale
English: B2 (Upper Intermediate) — mandatory
Based in Portugal — mandatory. Portuguese nationals or residents strongly preferred.
NICE TO HAVE
Experience with LLM fine-tuning or RAG pipelines — any meaningful GenAI exposure
Background in recommender systems or NLP, particularly relevant in matching and ranking contexts
Familiarity with Data Mesh or feature platform architecture patterns
Real-time model scoring and stream inference design
Knowledge of Responsible AI practices — fairness, bias detection, and explainability tooling
Domain experience in HRTech, marketplace platforms, or algorithmic matching systems
Compensation: €2,520 – €3,360/month net, depending on experience and seniority level.
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