Machine Learning Engineer (GCP)

Job Summary

We are looking for an experienced Machine Learning Engineer / MLOps Engineer to design, deploy, and maintain scalable machine learning solutions on Google Cloud Platform (GCP). The ideal candidate will have strong expertise in production ML systems, CI/CD automation, Vertex AI, and model lifecycle management while collaborating closely with cross-functional teams to operationalize machine learning models.

Key Responsibilities:

• • Design, build, and maintain training and inference pipelines for storm outage prediction workflows.
  • Implement CI/CD, orchestration, and automation for machine learning workflows using Vertex AI and related GCP services.
  • Deploy machine learning models into production environments and manage model lifecycle processes, including versioning and rollout support.
  • Set up and maintain baseline monitoring for model drift, performance, reliability, and alerting.
  • Create scalable, production-ready ML workflows and supporting technical documentation.
  • Collaborate with data scientists, engineers, and project stakeholders to operationalize models and align deployment architecture with project needs.
  • Support troubleshooting, performance tuning, and continuous improvement of ML platform components.
  • Contribute to engineering best practices across code quality, release processes, and environment stability.

Required Qualifications:

• • 5-10 years of experience in machine learning engineering, MLOps, or related production ML engineering roles.
  • Strong proficiency in Python and experience developing scalable data and ML workflows.
  • Hands-on experience with Google Cloud Platform, including Vertex AI, BigQuery, Cloud Storage, Cloud Run, Cloud Functions, or comparable services.
  • Experience with GitHub Actions or similar CI/CD tooling.
  • Demonstrated experience building CI/CD pipelines and automating ML model deployment and orchestration.
  • Experience with model monitoring, observability, and production support for machine learning systems.
  • Strong understanding of software engineering best practices, including version control, testing, and documentation.
  • Ability to work effectively across distributed teams and communicate clearly with technical and non-technical stakeholders.

Preferred Qualifications:

• • Experience supporting forecasting, outage prediction, or other data-intensive operational use cases.
  • Familiarity with utility, energy, weather, or geospatial data domains.
  • Exposure to model registry, retraining automation, and ML lifecycle governance practices.
  • Prior experience working in offshore or globally distributed delivery models.