Google Cloud AI Architect

• Solid machine learning knowledge: Mastery of machine learning algorithms, techniques, and frameworks.
• Programming experience: Proficiency in Python and other relevant programming languages.
• Familiarity with GCP: Knowledge of GCP services and tools for machine learning.
• Analytical and problem-solving skills: Ability to analyze complex problems and find effective solutions.
• Communication and collaboration: Ability to work in a team and communicate clearly and concisely.

Permanent contracts.
Work-life balance measures: flexible working hours, intensive working day on Fridays and during the summer months, flexible working space measures, assistance and administrative procedures for maternity and paternity leave, participation in training activities, etc.).
Access to a Flexible Remuneration Plan (Health Insurance, Childcare Vouchers, Restaurant Vouchers, Transport Card, and Training).
Free physiotherapy service at the headquarters.
Assignment of an HRBP to accompany you throughout your career, to support your professional and personal development during your service.
English classes.
Resources for Devoteamers (offering employees discounts on the purchase of goods, ticket reservations, travel, etc.) The Devoteam Group promotes equal opportunities, promotes its employees based on merit, and actively fights against all forms of discrimination. We are convinced that diversity and equality contribute to the creativity, dynamism, and excellence of our organization, contributing to the advancement of society. All our positions are open to everyone.

At Devoteam, we are looking for an ML Architect with a detailed description of the key tasks and functions that a Google Cloud Machine Learning Engineer would carry out:

Model design and development

• Problem and requirements analysis: Collaborate with multidisciplinary teams to understand business problems and translate them into viable machine learning solutions.
• Selection of algorithms and techniques: Choose the most appropriate machine learning algorithms and techniques to address specific problems.
• Model development and training: Use GCP tools and frameworks (such as TensorFlow, Keras, scikit-learn) to develop, train, and evaluate machine learning models.
• Model optimization: Tune hyperparameters and model architectures to improve their performance and accuracy.

• Data collection and cleaning: Gather data from various sources and perform cleaning and preprocessing tasks to ensure its quality.
• Exploratory data analysis: Explore and understand the data to identify patterns, trends, and anomalies.
• Feature engineering: Transform and create relevant features to improve model performance.
• Data storage and management: Use GCP storage services (such as BigQuery, Cloud Storage) to store and manage large volumes of data efficiently.
• Model deployment to production: Implement trained models in production environments using GCP services (such as AI Platform, Cloud Functions).
• Model monitoring and maintenance: Monitor model performance in production and perform updates and adjustments as needed.
• Model scalability: Ensure models can handle large volumes of data and requests in real time.
• Process automation: Use automation and orchestration tools (such as Cloud Composer, Kubeflow) to streamline machine learning workflows.
• Teamwork: Collaborate closely with data scientists, software engineers, solution architects, and other professionals.
• Effective communication: Present findings and results clearly and concisely to technical and non-technical audiences.
• Documentation: Maintain complete and up-to-date documentation of machine learning projects.
• Staying up to date: Keep up with the latest trends and advances in the field of machine learning.
• Experimenting with new technologies: Explore and evaluate new machine learning tools and techniques.
• Contributing to the community: Share knowledge and experiences through publications, presentations, and open-source projects.