- Experience: 3-5 years
- Location: Bangalore, India
- Platform: Google Cloud Platform (GCP)
- Domain: AI/ML, Digital Media, AdTech, Audience Analytics
- 100% Hands-on MLOps Engineering Role
Role Summary
- Build and operate enterprise MLOps and LLMOps platforms on Google Cloud Platform. Support AI products across audience intelligence, content recommendation, advertising optimization, content intelligence and personalization use cases within digital media environments.
Key Outcomes (First 6-12 Months)
- Establish production-grade MLOps pipelines on Vertex AI.
- Deploy scalable ML and GenAI models with strong observability.
- Improve model deployment frequency and reliability.
- Implement monitoring, governance and automated retraining processes.
Responsibilities
- Design and implement CI/CD/CT pipelines using Vertex AI Pipelines and Kubeflow.
- Manage model lifecycle from experimentation to production deployment.
- Build automated model training, evaluation, deployment and monitoring workflows.
- Implement model observability, drift detection and performance monitoring.
- Support GenAI, Agentic AI and recommendation engine deployments.
- Integrate real-time and batch data pipelines using Pub/Sub, BigQuery and Dataflow.
- Collaborate with Data Scientists,
Data Engineers and Platform Engineers.
- Optimize performance, scalability and infrastructure costs.
GCP AI &
- MLOps Stack
- Vertex AI
- Vertex AI Pipelines
- Model Registry
- Feature Store
- BigQuery ML
- Dataflow
- Pub/Sub
- Cloud Storage
- GKE
- Artifact Registry
- IAM
- Secret Manager
- Cloud KMS
- Model Governance
- Responsible AI
- Audit Logging
- Compliance Controls
Qualifications
- Bachelor degree in Engineering, Computer Science or equivalent
- 3-5 years experience in MLOps or ML Platform Engineering
- Robust Python development skills
- Experience with GCP and Vertex AI preferred