Innovation starts from the heart. At Edwards Lifesciences, we’re dedicated to developing ground-breaking technologies with a genuine impact on patients’ lives. At the core of this commitment is our investment in cutting-edge information technology. This supports our innovation and collaboration on a global scale, enabling our diverse teams to optimize both efficiency and success. As part of our IT team, your expertise and commitment will help facilitate our patient-focused mission by developing and enhancing technological solutions.

How you will make an impact:

Participate in agile development processes, including sprint planning and daily stand-upsCollaborate with cross-functional teams (data scientists, software engineers, product managers, business leads) to define requirements and deliver high-quality ML solutions.Conduct demos to showcase progress and gather feedback.Conduct research on open-source tools and ML techniques relevant to the medical domainDesign, implement, and optimize generative AI solutions (eg:, chatbots, content generators, code assistants).Lead the development and deployment of scalable and efficient ML models in production environments as well as fine tune large language models (LLMs).Drive research and experimentation to explore new ML techniques, tools, and frameworks.Build end-to-end data pipelines for collecting, processing, and analyzing large-scale datasets.Mentor junior engineers and contribute to the development of team processes and best practices.Stay up-to-date with the latest trends and advancements in machine learning and AI.

 
What you'll need (Requirements):

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related field (PhD is a plus).5+ years of professional experience in machine learning engineering, with a strong focus on deploying machine learning models in production environments.Proficiency in programming languages such as Python, Java, C++, or similar.Experience with GenAI models (eg:, GPT, BERT, T5, DALL-E, Stable Diffusion)Experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, Keras, etc.).Experience with prompt engineering and model fine tuning.Experience with cloud platforms such as AWS or Azure for model deployment and data storage.Familiarity with big data technologies like Hadoop, Spark, or similar tools is a plus.Solid experience with version control systems (Git) and agile development methodologies.Strong communication skills and the ability to work effectively in cross-functional teams.

What else we look for (Preferred) :

Experience with deep learning techniques (e.g., CNNs, RNNs, GANs, etc.).Familiarity with MLOps and tools for model deployment and monitoring (e.g., MLflow, Kubeflow, Docker, Kubernetes).Expertise in natural language processing (NLP) or computer vision (CV) applications is a plus.Knowledge of data engineering practices and tools like Apache Kafka, Airflow, etc.Experience deploying models in production environments.