Senior Machine Learning Engineer (MLOps/TensorFlow) - Remote Portugal

ABOUT THE OPPORTUNITY

Join a world-class technology consultancy as a Machine Learning Engineer, working closely with the ML Architect to develop scalable ML frameworks and experimentation platforms. You'll build large-scale distributed machine learning systems that are performant, efficient, and reliable while collaborating with cross-functional teams to deploy and integrate models across business units. This role offers you the opportunity to optimize ML pipelines, manage feature stores, and contribute to evaluating cutting-edge technologies that enhance machine learning capabilities.

PROJECT & CONTEXT

You'll develop and maintain large-scale distributed machine learning systems using frameworks like TensorFlow, PyTorch, and Scikit-Learn. The role involves building predictive models including churn prediction, user journey analysis, and sales forecasting using behavioral data. You'll work with supervised and unsupervised learning, survival analysis, time series modeling, and statistical forecasting techniques. Collaborating with business units, you'll understand their ML needs and work on cross-BU ML portfolio initiatives. You'll optimize feature extraction, transformation, and selection while managing Feature Stores for reusability across ML pipelines. Strong focus on MLOps practices including model training, versioning, monitoring, and deployment using CI/CD pipelines, Docker, Kubernetes, Airflow, SageMaker, and MLflow. You'll ensure scalability, reliability, cost efficiency, and ease of use of the machine learning platform while maintaining model observability and connecting outcomes to product and strategic goals.

WHAT WE'RE LOOKING FOR (Required)

• 5+ years Machine Learning Engineering experience building production ML systems
• ML techniques expertise: Strong experience with supervised and unsupervised learning, survival analysis, time series modeling, and statistical forecasting
• Predictive modeling: Skilled in building models such as churn prediction, user journey analysis, and sales forecasting using behavioral data
• ML frameworks proficiency: Expert with TensorFlow, PyTorch, or Scikit-Learn for model development
• Model lifecycle management: Experienced in model training, versioning, deployment, and monitoring in production
• MLOps practices: Solid background in CI/CD pipelines, Docker, Kubernetes, Apache Airflow, AWS SageMaker, MLflow, and model observability tools
• Feature engineering: Experience with feature stores and optimizing feature extraction, transformation, and selection
• Distributed systems: Ability to develop large-scale distributed ML systems that are scalable, performant, and reliable
• Business mindset: Ability to connect model outcomes to product goals and strategic business objectives
• Cross-functional collaboration: Experience working with business units and cross-functional teams to deploy and integrate ML models
• Language requirement: Fluent English (mandatory)

NICE TO HAVE (Preferred)

• Experience with additional cloud platforms (Azure, GCP) for ML workloads
• Knowledge of advanced deep learning architectures and techniques
• Familiarity with experiment tracking and A/B testing platforms
• Experience with real-time ML inference systems
• Contributions to open-source ML projects or research publications
• Background in specific domains (e-commerce, fintech, recommendation systems)