AI Engineer
Board of Innovation
DockerKubernetesPythonPyTorchScikit-Learn
Senior ML Engineer – Personalisation
Beyond
contract
Posted on:
Location Type: Remote
Location: Remote • 🇵🇹 Portugal
Visit company websiteJob Level
Senior
Tech Stack
AWSCloudDistributed SystemsDockerGoogle Cloud PlatformGraphQLKubernetesPythonPyTorchScikit-LearnTensorflow
About the role
- Develop scalable, high-performance personalization backend systems and engines, including the ingestion and processing of data to create AI driven personalization pipelines.
- Bring to life industry leading personalization technologies, leveraging traditional and advanced Machine Learning models, as well as large language models (LLM) to improve user experience through personalization.
- Implement approaches to enhance the performance, reliability, and observability of personalization services, ensuring low-latency, high-availability systems.
- Implement frameworks for evaluating personalization quality through both offline metrics and live A/B experimentation.
- Champion engineering best practices and mentor engineers across teams, raising the bar for code quality and system design.
- Staying ahead of trends in personalization and recommendation technologies, distributed systems, and bringing these innovations into production.
Requirements
- Degree in Computer Science, Engineering, or a related technical field.
- 6+ years of experience in developing large-scale distributed backend systems.
- Hands-on experience with personalization infrastructure and/or recommendation engines is a strong advantage.
- Deep expertise in Collaborative Filtering (user-item, item-item), Content-Based Filtering, and Matrix Factorization techniques (SVD, ALS).
- Experience developing advanced models, such as:
- - Deep Learning Architectures: Including Two-Tower models for scalable candidate retrieval, and sequence-aware models like Transformers or RNNs for session-based recommendations.
- - Hybrid Models: Combining multiple approaches (e.g., collaborative and content-based) to overcome their individual limitations.
- Developing specialized Techniques, such as:
- - Multi-Armed Bandits (for exploration vs. exploitation)
- - Learning to Rank (LTR) for optimizing ordered lists
- - Generating embeddings for users and items
- Mastery of Python and its core ML ecosystem, including TensorFlow, PyTorch, Scikit-learn, and XGBoost.
- Demonstrable experience building robust APIs (REST, GraphQL) and operating in modern cloud environments (GCP, AWS), using Kubernetes, Docker, CI/CD, and observability tools.
- Proven ability to influence engineering direction across teams and functions.
- Strong communication skills, being able to engage diverse technical stakeholders.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
Machine LearningDeep LearningCollaborative FilteringContent-Based FilteringMatrix FactorizationTwo-Tower modelsTransformersRNNsMulti-Armed BanditsLearning to Rank
Soft skills
mentoringinfluencingcommunication
