Join neXa – Let’s Shape the Future Together!
Position: Machine Learning Engineer
At neXa, we’re not just building digital solutions — we’re helping businesses grow smarter. We work with forward-thinking clients across industries to design, build, and implement technology that makes a real difference. From intelligent automation to custom applications, our projects are as diverse as our team.
Join neXa as a Machine Learning Engineer and take a central role in designing, developing, and deploying Machine Learning solutions for a high-scale advertising platform. You’ll work closely with Data Scientists, Software Engineers, and Product teams to create end-to-end ML pipelines, from feature engineering and model training to online deployment and monitoring. This hybrid role gives you the opportunity to work on cutting-edge ML solutions at scale while directly impacting the performance and value of a leading digital platform.
Join a friendly, growth-focused environment where innovation is not a buzzword — it’s how we work.
Scroll down to see the full job description, including responsibilities and requirements:
Responsibilities:
- Design, develop, optimize, and maintain data and Machine Learning training pipelines.
- Collaborate with Data Scientists, Software Engineers, Product, and Data teams across the entire ML model lifecycle.
- Manage production-running models, including monitoring, tuning, and performance optimization.
- Ensure high availability, scalability, and robustness of ML solutions in production environments.
- Take full responsibility for tasks across their lifecycle: engineering requirements, implementation, deployment, and maintenance.
- Contribute to best practices in model deployment, observability, and online inference pipelines.
Requirements:
- Bachelor’s or Master’s degree in Machine Learning, Mathematics, Computer Science, Statistics, or a related field.
- Strong proficiency in Python, including microservices and ML libraries development.
- Hands-on experience with ML model lifecycle management and practical ML-based solutions development.
- Experience with large-scale computation on cloud platforms (GCP, AWS, or Azure).
- Practical knowledge of SQL for data processing and analysis.
- Understanding of ML algorithms and common libraries for model development and deployment.
- Ability to independently make technical decisions and take full responsibility for assigned tasks.
- English proficiency at B2 or higher.
Nice to Have:
- Experience with ML frameworks like PyTorch or TensorFlow.
- Familiarity with PySpark, Pandas, GCP services (BigQuery, Composer, Vertex AI, Dataproc, Looker Studio).
- Previous experience building end-to-end ML pipelines for high-scale systems.