Designs, develops, and maintains end-to-end machine learning and data pipelines, ML services, APIs, and production deployments. Implements MLOps practices including CI/CD, testing, monitoring, quality controls, and model lifecycle management. Works with AWS SageMaker, Docker, Kubernetes, and GenAI solutions while troubleshooting production systems and making architectural decisions. Partners with data scientists, mentors team members, and contributes in an Agile/Scrum environment.
Alongside is a Portuguese company that partners with international organizations to build and scale exceptional tech teams.
We are looking for a Machine Learning Engineer to join a project with one of our clients, a global leader in professional information solutions and software. Operating across more than 180 countries, the company develops technology-driven solutions for industries including Healthcare, Tax & Accounting, Financial & Corporate Compliance, and Legal & Regulatory.
Responsibilities:
- Design, develop, and maintain scalable data and end-to-end ML pipelines, from data ingestion to production deployment.
- Build and deploy ML services and APIs, ensuring reliability, scalability, and performance.
- Partner with Data Scientists to transform models into robust, production-ready solutions.
- Implement MLOps best practices, including CI/CD, testing, data/code quality, monitoring, and model lifecycle management.
- Troubleshoot production ML systems and drive technical and architectural decisions.
- Work with AWS ML services, particularly SageMaker, and containerized environments using Docker/Kubernetes.
- Contribute to GenAI implementations within our platform framework.
- Mentor team members and contribute to delivery in an Agile/Scrum environment.
Requirements
- Degree in Computer Engineering, IT, or a related field.
- 5+ years of experience in Backend Engineering and/or Machine Learning Engineering.
- Strong production-level Python development skills.
- Hands-on experience building E2E ML pipelines and deploying ML models through APIs.
- Experience with SQL/NoSQL databases, testing frameworks, and data/code quality practices.
- Experience with MLOps tools such as MLflow, Kubeflow, or similar.
- Strong knowledge of AWS, particularly SageMaker and related ML services.
- Experience with Docker and Kubernetes.
- Strong understanding of ML model deployment and lifecycle management.
- Fluent English and strong communication and technical decision-making skills.
Nice to Have - Experience with Computer Vision, NLP, TensorFlow/PyTorch, Scikit-Learn, Pandas, Terraform/CloudFormation, asynchronous messaging, and ML monitoring/observability tools.
Benefits
- Hybrid working model: 2 days per week at the office (Porto);
- Collaborative and international work environment;
- Opportunity to work on impactful software products and transformation projects;
- Exposure to modern technologies, tools, and development practices;
- Opportunity to collaborate with experienced professionals across different countries and areas of expertise.
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