We're Hiring : MLOps Engineer (Google Cloud Platform)

Location : Bangalore

Experience : 5+ Years | Full-time

Industry : AI/ML | Cloud Engineering | Data Platforms

About the Role :

We are looking for an experienced MLOps Engineer who will play a critical role in designing and maintaining scalable, reliable, and automated machine learning infrastructure on Google Cloud Platform (GCP). You will work closely with Data Scientists, Data Engineers, and Business SMEs to deploy ML models and pipelines in production, ensuring reproducibility, governance, and monitoring at scale.

What You'll Do :

- Build and maintain ML pipelines using Vertex AI Pipelines and Terraform for infrastructure-as-code (IaC).

- Develop and manage CI/CD pipelines for model training, deployment, and monitoring using Cloud Build, Git, and Cloud Source Repositories.

- Package and deploy ML models with Docker on GKE (Google Kubernetes Engine) or Cloud Run.

- Implement robust logging, monitoring, and alerting using Cloud Monitoring and Cloud Logging.

- Design reusable templates for experimentation, model serving, and orchestration.

- Automate data source integration, especially with BigQuery.

- Define and enforce ML lifecycle governance from experimentation through decommissioning.

- Collaborate with stakeholders to map responsibilities, define lifecycle stages, and instill best practices.

- Engage in agile development sprints, ensuring models are deployed with high quality and robustness.

- Conduct knowledge transfer sessions, documentation, and internal training.

- Stay up-to-date with the latest in cloud and MLOps technologies.

What Were Looking For :

- 5+ years of experience in MLOps, ML Engineering, or Data Engineering roles.

- Proven expertise in GCP services like Vertex AI, BigQuery, Cloud Storage, and Cloud Functions.

- Strong hands-on experience with Python, Terraform, CI/CD tools (e.g., GitHub Actions, Cloud Build), Docker, and Kubernetes.

- Experience with full ML model lifecycle management, including experimentation tracking, monitoring, and versioning.

- Familiarity with MLOps best practices : reproducibility, automation, governance, and scalability.

Good to Have :

- Exposure to Kubeflow, MLflow, or similar MLOps platforms.

- Experience working in regulated industries (e.g., BFSI, Healthcare) with audit/compliance requirements.

- Background in time series forecasting or other complex ML workflows.

Why Join Us ?

- Work with a cutting-edge ML stack in a cloud-first, automation-driven environment.

- Collaborate with top talent in AI, cloud, and data engineering.

- Be a part of a company that prioritizes innovation, governance, and scalable machine learning.