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Senior Machine Learning Engineer
Skills
A/B TestingPersonalizationPrivacy RegulationsProduct PromotionRecommender SystemsSoft SkillsWritten
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Master's or Ph.D. in Data Science Computer Science Statistics or a related field
- 5+ years of industry experience in building and deploying machine learning models with a focus on recommendation systems and personalization engines
- Hands-on experience in delivering machine learning models to production at scale
- Experience in writing production quality Python code
- Comfortable working with Python data science and machine learning libraries such as scikit-learn TensorFlow Keras pandas numpy PyTorch XGBoost
- Experience with common LLM frameworks (Langchain Llamaindex RAG HuggingFace and eval frameworks like Ragas or Presidio)
- Strong understanding of machine learning applications development life cycle processes and tools CI/CD version control (git) testing frameworks MLOps agile methodologies monitoring and alerting
- Comfortable working with Docker and containerised applications
- Experience with A/B testing and experimentation in a production environment
Day to day
- Apply state of the art machine learning techniques including deep learning reinforcement learning causal inference and optimization to design and build recommendation models and personalization engines tailored to our users' preferences
- Create the tools frameworks and libraries that enable the acceleration of our ML product delivery
- Drive improvements to our current AI workflows in terms of process performance and testing
- Proven experience setting up and optimizing retrieval augmented generation RAG pipelines
- Present findings insights and solutions to the product team translating complex technical concepts into business language
- Participate in the full software development cycle design develop QA deploy experiment analyze and iterate
- Mentor junior engineers and foster a collaborative innovative team environment
- Establish thought-leadership by following up around state of the art research in responsible AI and Gen AI