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.