Senior Engineer, AI & Machine Learning

Edwards Lifesciences is expanding its global technology capabilities with the launch of a new IT Innovation Hub in Pune, India. The Hub strengthens the technology foundation that supports our business worldwide, working in close partnership with teams across regions to deliver reliable, high-quality solutions at scale.

Designed as a long-term investment, the Pune Hub will advance newer digital capabilities such as automation, data, AI, and cloud, and will include roles spanning end user services, application development, and enterprise platform teams. Together, these teams will help Edwards operate more efficiently

and accelerate innovation in support of our mission of advancing patient care.

AI Factory team focuses in developing and delivering cutting edge technologies to improve patient care. Our vision is to provide proactive care and smart recovery to our patient community who are fighting cardiovascular disease through innovative connected solutions.

In this role you will be working alongside Product Engineering Leads in developing various desktop, web and mobile software fueled by a robust AI engine that will improve patient care. We look for candidates who are not only technically competent but can share the same pride we feel for the work we do to serve our patient community.

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.

  
  

• Develop and implement end-to-end AI/ML models and workflows within the Palantir Foundry and AIP environment

  
  

• Leverage Palantir AIP features, such as Agent Studio, RAG (retrieval-augmented generation) workflows, and copilots, to ground models in relevant data sources and the Ontology.

  
  

• Conduct research on open-source tools and ML techniques relevant to the medical domain

  
  

• Design, implement, and optimize advanced machine learning algorithms to solve complex business problems.

  
  

• Lead the development and deployment of scalable and efficient ML models in production environments.

  
  

• 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 (Required)

• 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.

  
  

• 1+ years of hands on experience with Palantir Foundry ecosystem and AIP

  
  

• Proficiency in programming languages such as Python, Java, C++, or similar.

  
  

• Experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, Keras, etc.).

  
  

• Strong understanding of machine learning algorithms, statistics, and data structures.

  
  

• Experience with cloud platforms such as AWS 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)

• Preferred certifications include Foundry Data Engineer Certification, Foundry Solution Architect Certification, or Foundry Application Developer Certification..

  
  

• 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).

  
  

• Knowledge of data engineering practices and tools like Apache Kafka, Airflow, etc.