ABOUT THE OPPORTUNITY
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Join a forward-thinking technology company building cutting-edge machine learning solutions that power real-world applications at scale. This position offers the chance to work on production-grade ML systems, taking ownership of the full lifecycle from model development through deployment, monitoring, and continuous optimization. You'll be part of a team that values technical excellence, innovation, and the practical application of machine learning to solve meaningful business challenges.
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This is a fully remote role based in Portugal.
You'll work in an environment that emphasizes MLOps best practices, ensuring your models don't just work in notebooks but deliver measurable value in production environments. The role offers excellent growth opportunities as you'll gain exposure to diverse ML use cases, modern deployment architectures, and the latest tools in the ML engineering ecosystem.
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PROJECT & CONTEXT
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You'll be building and deploying production-ready machine learning models across various applications including recommendation engines, predictive analytics systems, and classification solutions. Your work will span the complete ML lifecycle - from collaborating with Data Engineers on data preparation and feature engineering, through model training and optimization, to building robust MLOps pipelines that ensure your models perform reliably in production.
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The role emphasizes the engineering side of machine learning - you'll spend significant time on model deployment, versioning, monitoring for data drift and model decay, automated retraining pipelines, and infrastructure management. You'll work with cloud-native architectures and containerization technologies to ensure your models can scale effectively and maintain high availability. This is an ideal opportunity for ML practitioners who want to bridge the gap between data science experimentation and production engineering.
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Core Tech Stack: Python, TensorFlow, PyTorch, Scikit-learn, XGBoost
\nMLOps Toolkit: MLflow, Kubeflow, Docker, Kubernetes
\nCloud Platforms: AWS SageMaker, Azure ML, or Google AI Platform
\nFocus Areas: Model deployment, monitoring, performance optimization, and production reliability
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WHAT WE'RE LOOKING FOR (Required)
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\n- ML Fundamentals: Strong background in machine learning algorithms, statistics, and model evaluation techniques
\n- Python Expertise: Proficiency in Python for ML development, including experience with data manipulation libraries (pandas, NumPy)
\n- TensorFlow/PyTorch: Deep expertise in at least one major ML framework (TensorFlow or PyTorch) with production experience
\n- Model Development: Proven experience designing, training, and evaluating supervised, unsupervised, and deep learning models
\n- Feature Engineering: Strong skills in feature selection, transformation, and engineering to improve model performance
\n- MLOps Practices: Hands-on experience building ML pipelines for deployment, versioning, and monitoring
\n- Containerization: Working experience with Docker for model packaging and deployment
\n- Cloud Platforms: Practical experience with at least one cloud ML platform (AWS SageMaker, Azure ML, or Google AI Platform)
\n- Data Engineering Basics: Understanding of data pipelines, ETL processes, and working with databases
\n- Model Optimization: Experience with hyperparameter tuning, model performance optimization, and efficiency improvements
\n- Production Mindset: Track record of deploying and maintaining models in production environments with monitoring and alerting
\n- Language: B2+ English level (Upper Intermediate minimum) for technical communication and documentation
\n- Location: Based in Portugal with availability for fully remote work
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NICE TO HAVE (Preferred)
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\n- Kubernetes Orchestration: Experience deploying ML workloads on Kubernetes with autoscaling and resource management
\n- MLOps Tools: Hands-on experience with Kubeflow, MLflow, or similar MLOps platforms for experiment tracking and model registry
\n- Advanced ML Frameworks: Experience with XGBoost, LightGBM, CatBoost for gradient boosting applications
\n- Multiple Frameworks: Proficiency in both TensorFlow and PyTorch with understanding of their respective strengths
\n- Alternative Languages: Knowledge of R or Scala for specific ML workflows
\n- Model Monitoring: Experience implementing data drift detection, model decay monitoring, and automated retraining triggers
\n- CI/CD for ML: Building automated testing and deployment pipelines specifically for ML models
\n- Distributed Training: Experience with distributed model training across multiple GPUs or nodes
\n- Feature Stores: Familiarity with feature store solutions (Feast, Tecton, AWS Feature Store)
\n- A/B Testing: Experience with experimentation frameworks and statistical methods for model evaluation in production
\n- GPU Optimization: Understanding of GPU acceleration and optimization techniques for deep learning
\n- Streaming ML: Experience with real-time model serving and online learning scenarios
\n- AutoML Tools: Familiarity with automated machine learning platforms and neural architecture search
\n- Model Explainability: Experience with interpretability tools (SHAP, LIME) and model transparency practices
\n- Big Data Tools: Experience with Spark MLlib or Dask for large-scale ML workflows
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Certifications (Advantageous):
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\n- AWS Certified Machine Learning - Specialty
\n- Google Professional Machine Learning Engineer
\n- Microsoft Certified: Azure AI Engineer Associate
\n- TensorFlow Developer Certificate
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Location: Portugal (Fully Remote)
