Role overview
**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**
.