- Contribute to the design, implementation, and ongoing improvement of ML Ops processes to enable the development and effective usage of AI and Machine Learning (ML) systems within Multiplan.
- Contribute to the design and development of reusable tools (python-based) and processes to support easy application of best practices in areas such as Responsible AI
- Partner with data science developers to build and test AI/ML systems and deploy them to production.
- Support model monitoring activities to maintain the health of AI/ML systems in operation.
- Contribute to the operation and administration of our AI/ML development platform.
- Communicate and collaborate within the data science team and across other departments.
- Ensure compliance with HIPAA regulations and requirements.
- Demonstrate the company's core competencies and values.
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Machine Learning Operations Engineer - Atlanta, United States - MultiPlan
Description
Imagine a workplace that encourages you to interpret, innovate and inspire. Our employees do just that by helping healthcare payers manage the cost of care, improve competitiveness and inspire positive change. You can be part of an established company with a 40-year legacy that helps our customers thrive by interpreting our client's needs and tailoring innovative healthcare cost management solutions.
Our commitment to diversity, inclusion, and belonging are part of the fabric of our company. We strive to create a workplace that fosters mutual respect and collaboration, where every talented individual can participate and perform their best work. We are MultiPlan, and we are where bright people come to shine
Job Summary:
A successful Machine Learning Operations Engineer 2 candidate will join our growing data science team to support the creation and implementation of processes and technologies to make the development, deployment, and operation of AI and Machine Learning systems more efficient, effective, and responsible. Reporting to the Director of Machine Learning Operations, this position is responsible for supporting the creation and implementation of processes and technologies to make the development, deployment, and operation of AI and Machine Learning systems more efficient, effective, and responsible.
The candidate brings expertise with general data science concepts and methodologies along with experience operating AI/ML systems in a production environment. A successful candidate should demonstrate familiarity in core statistical and machine learning concepts such as probability theory, hypothesis testing, gradient boosting and other ensemble techniques, hyperparameter tuning, and model validation techniques. Familiarity with operational technologies such as containers and Kubernetes is also expected. Significant experience and comfort working in Python and utilizing common data storage systems (SQL, NoSQL, S3, etc.) is required.
Job Roles and Responsibilities:
Support production deployment and operation of data science applications, with particular emphasis on AI/ML systems. The successful candidate will also be responsible for contributing to the data science platform operations, AI/ML governance activities, and the development of new tools and techniques to support process improvements in areas such as responsible AI.