MLOps Engineer (GCP Specialization)

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 GCP s 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 GCP s 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).