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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying scalable ML pipelines and services, with a strong focus on MLOps best practices and AWS ML services. Proficient in Python development and experienced in collaborating with data scientists to deliver production-ready solutions.
Highest-signal resume keywords
Machine Learning EngineeringMLOps Best PracticesAWS SageMakerPython DevelopmentEnd-to-End ML Pipelines
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning EngineeringPython DevelopmentEnd-to-End ML PipelinesSQL DatabasesNoSQL DatabasesMLOps ToolsModel DeploymentData Quality PracticesTesting FrameworksML Lifecycle Management
Soft Skills
Strong CommunicationTechnical Decision-MakingMentoring
Tools & Technologies
AWSSageMakerDockerKubernetesMLflowKubeflowTensorFlowPyTorchScikit-LearnPandas
Industry Keywords
AgileScrumGenAIComputer VisionNLPTerraformCloudFormationAsynchronous MessagingML MonitoringObservability Tools
Tech Stack
Tools & technologiesAWSDockerKubernetesNoSQLPandasPythonPyTorchScikit-LearnSQLTensorflowTerraform
About the role
Key responsibilities & impact- 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 the platform framework
- Mentor team members and contribute to delivery in an Agile/Scrum environment
Requirements
What you’ll need- 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
Comp & perks- 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