Offer summary

Qualifications:

3+ years of experience in backend development with Python, including Django and Django REST Framework., Hands-on experience deploying machine learning models into production environments., Familiarity with Google Cloud Platform tools such as Cloud Run and Cloud SQL., Strong foundations in machine learning, including supervised and unsupervised learning techniques..

Key responsibilities:

Prototype predictive models and integrate them into the Django-based Customer Data Platform.

Design and develop scalable machine learning pipelines focusing on feature engineering and model training.

Deploy ML models using Google Cloud Platform tools for containerization and data management.

Collaborate with cross-functional teams to turn business requirements into production-grade AI solutions.

Job description

This is a remote position.

At ByTek, we believe in the transformative power of machine learning and data-driven platforms to empower businesses with predictive capabilities. Our company represents the MarTech division of the Datrix Group, an international ecosystem of B2B software companies powered by artificial intelligence and listed on the Italian stock exchange.

We design and deliver cutting-edge AI tools, including the Bytek Prediction Platform, to help organizations leverage their first-party data for smarter marketing and sales strategies.

We partner with top-tier banks, brands, and retailers in Italy and abroad, helping them harness the full potential of their data through scalable, AI-driven platforms. Our engineering culture is remote-first and collaborative, with hubs in Milan and Viterbo (Italy), New York (USA), and Dubai (UAE).

Join Bytek (Datrix Group) as a Machine Learning Engineer and help build our AI-powered Bytek Prediction Platform. You'll develop and deploy ML models to optimize marketing and sales for major clients. We seek 3+ years of Python/Django experience, GCP skills, and a passion for applying machine learning to real-world problems.

What you will do

Prototype predictive models and integrate them into our Django-based CDP platform to support features such as user scoring, segmentation, and automated targeting.
Design and develop scalable machine learning pipelines with a focus on feature engineering, model training, evaluation, and monitoring.
Deploy ML models using Google Cloud Platform tools including Cloud Run for containerization and Cloud SQL for relational data management.
Collaborate across teams (data science, backend, product) to turn business requirements into production-grade AI solutions.
Implement CI/CD pipelines with CI/CD tools to automate tests and deployment workflows.
Stay up to date with the latest developments in deep learning, MLOps, and scalable data infrastructure.

What we offer

Flexible work culture: 100% remote working approach, with opportunities to collaborate in-person across our offices in Milan, Viterbo, New York, and Dubai.
Competitive compensation: Full-time contract with base compensation 40.000 - 45.000€/year gross salary (RAL) + 10% bonus based on company performances..
Cutting-edge stack: Work with modern tools such as Django, Django REST Framework, GCP (Cloud Run, Cloud SQL, Artifact Registry), and ML frameworks including scikit-learn, XGBoost, TensorFlow, and PyTorch and agentic AI connection framework like MCP.
Real-world ML deployment: Apply your models directly into production systems as part of our proprietary Customer Data Platform (CDP).
Professional growth: Contribute to high-impact projects in AI, predictive modeling, and cloud architecture, supported by a multidisciplinary team of engineers, data scientists, and product experts.
Tools & perks: Company laptop, continuous learning budget, and access to internal AI/ML research.

Requirements

Proven experience: 3+ years in backend development with Python, including Django and Django REST Framework, and hands-on experience deploying machine learning models into production environments.
Cloud-native mindset: Experience with Google Cloud Platform, specifically Cloud Run, Cloud SQL, and Artifact Registry.
Strong ML foundations: Knowledge of supervised and unsupervised learning, model evaluation metrics, GLMs, regularization techniques, and modern feature engineering practices.
Advanced modeling skills: Familiarity with deep learning architectures (CNN, RNN, LSTM), embeddings, and transformers.
Pragmatic builder: You value simplicity and reliability in production systems, and thrive in cross-functional teams.
Curious and proactive: You enjoy exploring new technologies and applying them to real business problems.