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.
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