Edwards Lifesciences
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Pune · On-site · 5-8 Yrs · Aug 20
Health Care Products Manufacturing · 10000+ Employees · Irvine, US
Edwards Lifesciences (NYSE: EW), is the global leader of patient-focused medical innovations for structural heart disease and critical care monitoring. We are driven by a passion for patients, dedicated to improving and enhancing lives through partnerships with clinicians and stakeholders across the global healthcare landscape. Innovation. Relationships. Passion. Caring. It’s who we are. It’s personal. We thrive on discovery and expand the boundaries of medical technology. We are passionate about providing innovative solutions for people fighting cardiovascular disease. It's our Credo. Our corporate headquarters is located in Irvine, California, and we supply products and technologies to clinicians in nearly 100 countries around the world including North America, Latin America, Europe, Asia, and Australia. For our legal terms and trademarks, please visit: http://bit.ly/2fv28iO
Hiring: Engineering
What employees say
+ Great culture with genuinely approachable leadership and a collaborative work environment. People are willing to help, and there is a strong focus on teamwork rather than internal
− Cross-functional alignment occasionally requires patience, but this is generally expected in an enterprise environment.
More reviews on the company profile.
Interview experience · Director, India IT
I went through an intensive, multi-stage interview process for a Senior Level IT leadership role at the India hub. The loop required a significant investment of hours, including navigating multiple time zones for late-ni
Average difficulty · India · 2 reports
More interview insightsJob description
Overview
Innovation starts from the heart.
Responsibilities
- 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.
- 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.
Qualifications
- Strong understanding of machine learning algorithms, statistics, and data structures.
- 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.
- Preferred certifications include Foundry Data Engineer Certification, Foundry Solution Architect Certification, or Foundry Application Developer Certification..
- 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.
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