Machine Learning Engineer - MLOps
Department Overview
Data Science Center of Excellence (CoE) builds and supports the Data Science and Machine Learning Operations (ML Ops) elements of enterprise solutions that involve AI/ML (including Generative AI) capabilities and drive success for organization by creating material savings, efficiencies, and revenue growth opportunities. The team drives business innovation by leveraging emerging AI/ML capabilities and turning them into differentiating business capabilities.
Job Overview
Sustain business value at organization by leading the charge of Run & Sustain support of the Data Science and ML Ops elements of enterprise solutions that currently involve traditional ML and Generative AI capabilities.
Job Responsibilities
- Possess deep functional and technical understanding of the Machine Learning technologies (Google’s Cloud Platform, custom and COTS-embedded) and leverage it across organization’s large, complex and diverse landscape.
- Business acumen (strong understanding of how business operates, and how to harness data and analytics to meet business needs)
- Ability to develop and deploy advanced analytical models and algorithms that drive profitable growth, predictable patterns and areas of opportunity.
- Maintain a solid understanding of up and downstream impacts implementing solutions using ML technologies.
- Engage early in project efforts in order to analyze current solutions, provide solution options and recommendations, understand business process impact, provide accurate estimates.
- Aide adoption of ML solutions within the business by conducting training, coordinating demos.
- Establish development and delivery best practices in order for ML implementations to minimize rework and maintenance cost. Build ML architectures that work at organization scale.
- Work closely with business stakeholders and members of the build team (Agile Pod) to understand the Run & Sustain needs (including SLA, MTTR) of the solutions and the value that needs to be sustained.
- Leverage documentation created by the build team to gain knowledge of the Data Science and ML Ops elements of the solution.
- Maintain documentation of the solution during Run & Sustain
- Leverage strong understanding and working experience of the ML Ops lifecycle – feature engineering, continuous training, validation, scaling, deployment, HA, DR, monitoring, and feedback loop – to provide Run & Sustain support for ML-based solutions.
- Lead the Service Resolution Team (SRT) and leverage other roles like Data Engineer, Data Analyst and Visualization Engineers, to diagnose and resolve issues and ensure none to minimal impact to users of the solutions and to value of the solution. Priority should be on service restoration.
- Communicate issues with business impact relevant information to business and IT stakeholders of the solutions. Maintain periodic communication on status of the issue and expected resolution time/window.
- Perform Root Cause Analysis (RCA) on the issue and communicate that to the business and IT stakeholders of the solutions.
- Collaborate with the data scientists on model development to containerize and build out the deployment pipelines for new models.
- Collaborate with the data scientists on ML Ops life cycle relevant setup to ensure seamless Run & Sustain of the Data Science and ML Ops elements of the solutions.
- Build infrastructure and/or setup GCP services (like Cloud Functions) to support integration between AI/ML solutions and other solutions.
- Maintain at scale the APIs for consumption of machine learning models.
- Keep abreast of improvements in GCP Vert
Qualifications
- Bachelor's degree preferred or equivalent work experience.
- 7+ years in the distribution or Healthcare industry and deep knowledge of their business practices
- 5+ years of proven Machine Learning experience and involvement in packaged platform delivery and management.
- Strong working knowledge of a variety of machine learning techniques (Regression, Clustering, Decision Tree, Probability Networks, Neural Networks, Bayesian models etc.) along with Generative AI (preferable with Plam 2 or Gemini Pro)
- Experience with Machine Learning and related technologies such as Python, Tensorflow, Torch, Amazon SageMaker, Jupiter Notebooks.
- Understanding of cloud data engineering and integration concepts.
- Working experience with automated deployment and orchestration (CI/CD, Docker)
- Working experience with GCP services (GCE, GKE)
- Working experience in designing and optimizing ML models using GCP Vertex AI services
- Experience in Supervised and Unsupervised Machine Learning including Classification, Forecasting, Anomaly Detection, Pattern Detection, Text Mining, using variety of techniques such as Decision trees, Time Series Analysis, Bagging and Boosting algorithms, Neural Networks, Deep Learning.
- Excellent programming skills in Python and PySpark
- Have implemented ML Ops on Google’s GCP with particular focus on automated deployment and ensuring optimized performance.
- Have maintained and optimized the ML models developed by Data Scientists and ensured seamless deployment in different environments while enabling model tracking, model experimentation and model monitoring.
- Experience in using workflow management tools such as Airflow
- Experience of version control system (GitHub)
- Strong understanding of RESTful APIs and / or data streaming a big plus
- Google Cloud Platform certification is a plus
- Prefer experience with MLOps Agile practices
- Strong verbal and written communication skills especially relevant to Run & Sustain
Candidates who are back-to-work, people with disabilities, without a college degree, and Veterans are encouraged to apply.
Cardinal Health supports an inclusive workplace that values diversity of thought, experience and background. We celebrate the power of our differences to create better solutions for our customers by ensuring employees can be their authentic selves each day. Cardinal Health is an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, national origin, ancestry, age, physical or mental disability, sex, sexual orientation, gender identity/expression, pregnancy, veteran status, marital status, creed, status with regard to public assistance, genetic status or any other status protected by federal, state or local law.
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