Porto, Porto, Portugal
Actively hiring, first seen by us 4 days agoWork setup in the employer’s posting: On-siteFrom the employer’s posting
Employment type in the employer’s posting: Full-timeFrom the employer’s posting
Design and build end-to-end ML pipelines for training, testing and deployment
Turn data science prototypes into production-grade code (MLOps)
Monitor production models for performance, drift and reliability
Work with Data Engineers on data preparation and feature engineering
Design, develop, and implement end-to-end Machine Learning pipelines for training, testing, and deployment of predictive models.
Work closely with Data Scientists to translate prototypes and models into scalable, production-grade code (MLOps).
Implement monitoring and alerting solutions for models in production to track performance, detect drift, and ensure reliability.
Collaborate with Data Engineers to ensure efficient data preparation, feature engineering, and access to necessary data infrastructure.
3+ years of professional experience in a Machine Learning Engineer, AI Developer, or similar role.
Strong expertise in Python and object-oriented programming, with a focus on code quality and best practices.
Solid understanding of MLOps principles and the tools required for model deployment, versioning, and lifecycle management (e.g., MLflow, Kubeflow, or similar).
Proficiency in SQL and experience working with large datasets and data warehousing concepts.
Experience working with Cloud platforms (AWS, GCP, or Azure) for model deployment, compute, and storage (e.g., SageMaker, Vertex AI, Azure ML Services).
Knowledge of containerization technologies (Docker, Kubernetes) for building reproducible and scalable ML environments.
- Familiarity with distributed computing frameworks (e.g., Spark). - Advanced degree (M.S. or Ph.D.) in Computer Science, Engineering, or a related quantitative field. - Very good level of English, both spoken and written, for effective communication with international teams;
Actively hiringFirst seen by us 4 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 4
Early. Similar jobs usually come down between day 8 and day 76.
At Devoteam, jobs usually come down by about day 22.
From 5,460 similar postings that came down, and 152 of Devoteam’s. Coming down is not always a hire.
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Open jobs by the week we first saw them, last 12 weeks.
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- Work closely with Data Scientists to translate prototypes and models into scalable, production-grade code (MLOps). - Develop robust, efficient, and well-documented code primarily using Python and relevant ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). - Implement monitoring and alerting solutions for models in production to track performance, detect drift, and ensure reliability.
- 3+ years of professional experience in a Machine Learning Engineer, AI Developer, or similar role. - Strong expertise in Python and object-oriented programming, with a focus on code quality and best practices. - Mandatory practical experience with major Machine Learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn).
- Solid understanding of MLOps principles and the tools required for model deployment, versioning, and lifecycle management (e.g., MLflow, Kubeflow, or similar). - Proficiency in SQL and experience working with large datasets and data warehousing concepts. - Excellent analytical and problem-solving skills, with the ability to communicate complex technical concepts effectively.
- Excellent analytical and problem-solving skills, with the ability to communicate complex technical concepts effectively. - Experience working with Cloud platforms (AWS, GCP, or Azure) for model deployment, compute, and storage (e.g., SageMaker, Vertex AI, Azure ML Services). - Knowledge of containerization technologies (Docker, Kubernetes) for building reproducible and scalable ML environments.
- Experience working with Cloud platforms (AWS, GCP, or Azure) for model deployment, compute, and storage (e.g., SageMaker, Vertex AI, Azure ML Services). - Knowledge of containerization technologies (Docker, Kubernetes) for building reproducible and scalable ML environments. - Familiarity with distributed computing frameworks (e.g., Spark).
- Mandatory practical experience with major Machine Learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn). - Solid understanding of MLOps principles and the tools required for model deployment, versioning, and lifecycle management (e.g., MLflow, Kubeflow, or similar). - Proficiency in SQL and experience working with large datasets and data warehousing concepts.
- Knowledge of containerization technologies (Docker, Kubernetes) for building reproducible and scalable ML environments. - Familiarity with distributed computing frameworks (e.g., Spark). - Advanced degree (M.S. or Ph.D.) in Computer Science, Engineering, or a related quantitative field.
- Strong expertise in Python and object-oriented programming, with a focus on code quality and best practices. - Mandatory practical experience with major Machine Learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn). - Solid understanding of MLOps principles and the tools required for model deployment, versioning, and lifecycle management (e.g., MLflow, Kubeflow, or similar).
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New ML Engineer postings at the employers we follow are up 10% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
We are seeking a versatile and passionate AI / Machine Learning Engineer to join our data science and engineering team. You will be instrumental in bridging the gap between data science research and production-ready applications, building scalable machine learning systems that drive business value. This role requires a strong balance of software engineering principles and ML expertise.
Key Responsibilities
The Devoteam Group works for equal opportunities, promoting its employees based on merit and actively fights against all forms of discrimination. We are convinced that diversity contributes to the creativity, dynamism and excellence of our organization. All of our vacancies are open to people with disabilities.
At Devoteam, we believe that technology with strong human values can actively drive change for the better. Discover how Tech for People unlocks the future, creating a positive impact on the people and the world around us. We are a global leading player in Digital Transformation for leading organisations across EMEA, with a revenue of €1B. We believe in transforming technology to create value for our clients, partners and employees in a world where technology is developed for people. We are proud of the culture we have built together. We are proud of our people at the service of technology. We are proud of our diverse environment. Because we are #TechforPeople. Join our multidisciplinary team of Cloud experts, Designers, Business consultants, Security experts, Engineers, Developers and other extraordinary talents, spread across more than 20 EMEA countries. Become one of our +10.000 tech and business leaders on cloud, data and cyber security. Let’s fuse creativity with technology together and build innovative solutions that actively change things for the better.
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Porto, Porto, Portugal
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