MLOps Engineer (GCP Specialization)
MLOps Engineer (GCP Specialization)
Posted on :
29-07-2025
Employer Active
1 Vacancy
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Monthly Salary
Not Disclosed
Salary Not Disclosed
Vacancy
1 Vacancy
Posted on :
29-07-2025
Job Description
Role: MLOps Engineer
Locations: Nashville TN; Malvern PA (Onsite)
Technical Skills:
- Proficiency in programming languages such as Python.
- Expertise in GCP services including Vertex AI Google Kubernetes Engine (GKE) Cloud Run BigQuery Cloud Storage and Cloud Composer Data proc or PySpark and managed Airflow.
- Experience with infrastructure-as-code - Terraform.
- Familiarity with containerization (Docker GKE) and CI/CD pipelines GitLab and 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.
Responsibilities:
- Model Deployment: Design and implement pipelines for deploying machine learning models into production using GCP services such as AI Platform Vertex AI or Cloud Run Cloud Composer ensuring high availability and performance.
- Infrastructure Management: Build and maintain scalable GCP-based infrastructure using services like Google Compute Engine Google Kubernetes Engine (GKE) and Cloud Storage to support model training deployment and inference.
- Automation: Develop automated workflows for data ingestion model training validation and deployment using GCP tools like Cloud Composer and CI/CD pipelines integrated with GitLab and Bitbucket Repositories.
- Monitoring and Maintenance: Implement monitoring solutions using Google Cloud Monitoring and Logging to track model performance data drift and system health and take corrective actions as needed.
- Collaboration: Work closely with data scientists Data engineers Infrastructure and DevOps teams to streamline the ML lifecycle and ensure alignment with business objectives.
- Versioning and Reproducibility: Manage versioning of datasets models and code using GCP tools like Artifact Registry or Cloud Storage to ensure reproducibility and traceability of machine learning experiments.
- Optimization: Optimize model performance and resource utilization on GCP leveraging containerization with Docker and GKE and utilizing cost-efficient resources like preemptible VMs or Cloud TPU/GPU.
- Security and Compliance: Ensure ML systems comply with data privacy regulations (e.g. GDPR CCPA) using GCPs security tools like Cloud IAM VPC Service Controls and Data Loss Prevention (DLP).
- Tooling: Integrate GCP-native tools (e.g. Vertex AI Cloud composer) and open-source MLOps frameworks (e.g. MLflow Kubeflow) to support the ML lifecycle.
- Enable successful project delivery and customer satisfaction.
- Drive project and technology goals in compliance with organizational objectives.
Good to have skills:
- Experience with large-scale distributed ML systems on GCP such as Vertex AI Pipelines or Kubeflow on GKE Feature Store.
- Exposure to Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) applications and deployment strategies.
- Familiarity with GCPs model monitoring tools and techniques 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).
Employment Type
Full-time
Key Skills
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