DaVita Kidney Care
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This range is provided by DaVita Kidney Care. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $68,400.00/yr - $100,400.00/yr
Posting Date : 10/03/2025
Position Overview The MLOps Engineer (GCP Specialization) is responsible for designing, implementing, and maintaining infrastructure and processes on Google Cloud Platform (GCP) to enable the seamless development, deployment, and monitoring of machine learning models at scale. This role bridges data science and data engineering, infrastructure, ensuring that machine learning systems are reliable, scalable, and optimized for GCP environments.
Key Responsibilities
Design and implement pipelines for deploying machine learning models into production using GCP services such as AI Platform, Vertex AI, Cloud Run, or Cloud Composer.
Build and maintain scalable GCP-based infrastructure using services like Compute Engine, Kubernetes Engine, and Cloud Storage to support model training, deployment, and inference.
Develop automated workflows for data ingestion, model training, validation, and deployment using Cloud Composer and CI/CD pipelines integrated with GitLab and Bitbucket.
Implement monitoring solutions using Google Cloud Monitoring and Logging to track model performance, data drift, and system health.
Collaborate closely with data scientists, data engineers, infrastructure, and DevOps teams to streamline the ML lifecycle.
Manage versioning of datasets, models, and code using Artifact Registry or Cloud Storage to ensure reproducibility.
Optimize model performance and resource utilization on GCP, leveraging containerization with Docker and GKE, and utilizing cost‑efficient resources such as preemptible VMs or Cloud TPUs/GPUs.
Ensure ML systems comply with data privacy regulations (GDPR, CCPA) using Cloud IAM, VPC Service Controls, and Data Loss Prevention.
Integrate GCP‑native tools and open‑source MLOps frameworks (MLflow, Kubeflow) to support the ML lifecycle.
Qualifications Technical Skills
Proficiency in Python
Expertise in GCP services including Vertex AI, GKE, Cloud Run, BigQuery, Cloud Storage, Cloud Composer, and managed Airflow
Experience with infrastructure‑as‑code (Terraform)
Familiarity with containerization (Docker, GKE) and CI/CD pipelines (GitLab, Bitbucket)
Knowledge of ML frameworks (TensorFlow, PyTorch, scikit‑learn) and MLOps tools compatible with GCP (MLflow, Kubeflow) and Gen AI RAG applications
Understanding of data engineering concepts, including ETL pipelines with BigQuery and Dataflow, Dataproc Pyspark
Soft Skills
Strong problem‑solving and analytical skills
Excellent communication and collaboration abilities
Ability to work in a fast‑paced, cross‑functional environment
Preferred Qualifications
Experience with large‑scale distributed ML systems on GCP (Vertex AI Pipelines, Kubeflow on GKE, Feature Store)
Exposure to Generative AI and Retrieval‑Augmented Generation applications and deployment strategies
Familiarity with GCP’s model monitoring tools for detecting data drift or model degradation
Knowledge of microservices architecture and API development using Cloud Endpoints or Cloud Functions
Google Cloud Professional certifications (e.g., Professional Machine Learning Engineer, Professional Cloud Architect)
What We’ll Provide
Comprehensive benefits: medical, dental, vision, 401(k) match, paid time off, PTO cash out
Support for you and your family: family resources, EAP counseling, Headspace® access, backup child and elder care, maternity/paternity leave
Professional development programs: virtual leadership and development courses via DaVita’s training platform StarLearning
At DaVita, we strive to be a community first and a company second. We want all teammates to experience DaVita as a place where I belong. Our goal is to embed belonging into everything we do in our Village. We are proud to be an equal opportunity workplace and comply with state and federal affirmative action requirements. Individuals are recruited, hired, assigned and promoted without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, protected veteran status, or any other protected characteristic.
This position will be open for a minimum of three days.
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This range is provided by DaVita Kidney Care. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range $68,400.00/yr - $100,400.00/yr
Posting Date : 10/03/2025
Position Overview The MLOps Engineer (GCP Specialization) is responsible for designing, implementing, and maintaining infrastructure and processes on Google Cloud Platform (GCP) to enable the seamless development, deployment, and monitoring of machine learning models at scale. This role bridges data science and data engineering, infrastructure, ensuring that machine learning systems are reliable, scalable, and optimized for GCP environments.
Key Responsibilities
Design and implement pipelines for deploying machine learning models into production using GCP services such as AI Platform, Vertex AI, Cloud Run, or Cloud Composer.
Build and maintain scalable GCP-based infrastructure using services like Compute Engine, Kubernetes Engine, and Cloud Storage to support model training, deployment, and inference.
Develop automated workflows for data ingestion, model training, validation, and deployment using Cloud Composer and CI/CD pipelines integrated with GitLab and Bitbucket.
Implement monitoring solutions using Google Cloud Monitoring and Logging to track model performance, data drift, and system health.
Collaborate closely with data scientists, data engineers, infrastructure, and DevOps teams to streamline the ML lifecycle.
Manage versioning of datasets, models, and code using Artifact Registry or Cloud Storage to ensure reproducibility.
Optimize model performance and resource utilization on GCP, leveraging containerization with Docker and GKE, and utilizing cost‑efficient resources such as preemptible VMs or Cloud TPUs/GPUs.
Ensure ML systems comply with data privacy regulations (GDPR, CCPA) using Cloud IAM, VPC Service Controls, and Data Loss Prevention.
Integrate GCP‑native tools and open‑source MLOps frameworks (MLflow, Kubeflow) to support the ML lifecycle.
Qualifications Technical Skills
Proficiency in Python
Expertise in GCP services including Vertex AI, GKE, Cloud Run, BigQuery, Cloud Storage, Cloud Composer, and managed Airflow
Experience with infrastructure‑as‑code (Terraform)
Familiarity with containerization (Docker, GKE) and CI/CD pipelines (GitLab, Bitbucket)
Knowledge of ML frameworks (TensorFlow, PyTorch, scikit‑learn) and MLOps tools compatible with GCP (MLflow, Kubeflow) and Gen AI RAG applications
Understanding of data engineering concepts, including ETL pipelines with BigQuery and Dataflow, Dataproc Pyspark
Soft Skills
Strong problem‑solving and analytical skills
Excellent communication and collaboration abilities
Ability to work in a fast‑paced, cross‑functional environment
Preferred Qualifications
Experience with large‑scale distributed ML systems on GCP (Vertex AI Pipelines, Kubeflow on GKE, Feature Store)
Exposure to Generative AI and Retrieval‑Augmented Generation applications and deployment strategies
Familiarity with GCP’s model monitoring tools for detecting data drift or model degradation
Knowledge of microservices architecture and API development using Cloud Endpoints or Cloud Functions
Google Cloud Professional certifications (e.g., Professional Machine Learning Engineer, Professional Cloud Architect)
What We’ll Provide
Comprehensive benefits: medical, dental, vision, 401(k) match, paid time off, PTO cash out
Support for you and your family: family resources, EAP counseling, Headspace® access, backup child and elder care, maternity/paternity leave
Professional development programs: virtual leadership and development courses via DaVita’s training platform StarLearning
At DaVita, we strive to be a community first and a company second. We want all teammates to experience DaVita as a place where I belong. Our goal is to embed belonging into everything we do in our Village. We are proud to be an equal opportunity workplace and comply with state and federal affirmative action requirements. Individuals are recruited, hired, assigned and promoted without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, protected veteran status, or any other protected characteristic.
This position will be open for a minimum of three days.
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