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-ups
• Collaborate 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 domain
• Design, 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.