**Role: GCP MLOps Engineer (Retail or E-commerce domain)
Location: Katy, TX (Hybrid)
Duration: 12+ Months (C2C/W2)
Job Description:**
We are seeking a highly skilled
GCP ML Ops Engineer
to design, build, and manage scalable machine learning pipelines and production-grade infrastructure on
Google Cloud Platform
. The ideal candidate will have hands-on experience in
GCP services
,
machine learning model deployment
,
CI/CD automation
, and
containerization
.
Key Responsibilities:
- Build and manage end-to-end ML pipelines on GCP (data ingestion, model training, deployment, and monitoring).
- Automate model training and deployment workflows using Vertex AI , Kubeflow , or Cloud Composer .
- Implement CI/CD pipelines for ML models using Cloud Build , GitHub Actions , or similar tools.
- Develop scalable data pipelines using BigQuery , Dataflow , and Pub/Sub .
- Manage model versioning, logging, and performance tracking.
- Collaborate with Data Scientists and Cloud Engineers to productionize ML solutions.
- Ensure best practices in security, scalability, and cost optimization within GCP environments.
Required Skills:
- 3+ years of experience in GCP (must have hands-on experience with Vertex AI, BigQuery, Cloud Storage, Dataflow).
- Strong experience with ML Ops tools (Kubeflow, MLflow, TFX, or Vertex Pipelines).
- Proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Strong understanding of CI/CD , Docker , Kubernetes , and Terraform .
- Familiarity with monitoring tools (Stackdriver, Prometheus, Grafana).
- Experience with API integrations , data versioning , and model lifecycle management .
Nice to Have:
- Google Cloud Certified (Professional Data Engineer or ML Engineer).
- Exposure to DevOps or Data Engineering environments.
- Experience deploying ML solutions in retail or e-commerce domains .
Tags & focus areas
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