GCP MLOps Engineer

About the Role:

We are seeking a hands-on Machine Learning Engineer who will build, deploy and operationalise ML solutions using GCP and Vertex AI. You will partner with data scientists, data engineers, product teams, and cloud infrastructure teams to deliver scalable, production-ready ML systems that drive business impact.

Key Responsibilities:

• Design and build ML pipelines: data ingestion, feature engineering, model training, evaluation, deployment, monitoring.
• Use Vertex AI services such as CustomTraining, AutoML, Vertex AI Pipelines, Model Registry, Feature Store for end-to-end ML lifecycle.
• Leverage GCP services like BigQuery, Cloud Storage (GCS), Dataflow, Pub/Sub, Cloud Functions, etc., to support ML workflows.
• Deploy trained models as endpoints (online/batch inference) and set up monitoring for model drift, performance metrics, version control.
• Apply best-practices in MLOps: CI/CD for ML, containerisation (Docker), orchestration (Kubernetes/GKE or equivalent), infrastructure as code (Terraform/Deployment Manager).
• Work with cross-functional teams to translate business requirements into ML solutions, document architectures and decisions, ensure security/governance/compliance.

Required Skills & Experience:

• 5-10 years (or depending on seniority) of experience in ML engineering / software + ML production systems.
• Hands-on experience with GCP services and especially Vertex AI (training, pipelines, deployment, monitoring).
• Strong programming ability in Python; experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Experience in building data pipelines, feature engineering, working with large scale data.
• Familiarity with model deployment/service architecture (online vs batch), containerisation, orchestration.
• Good understanding of ML lifecycle, versioning, model governance, monitoring and operational issues.
• Comfortable collaborating in agile teams and working with product/engineering stakeholders.

Nice to Have:

• GCP certification such as Google Cloud Professional Machine Learning Engineer.
• Experience with GenAI / LLMs and Vertex AI Model Garden or RAG workflows.
• Exposure to infrastructure as code (Terraform), kubernetes, and cloud security/governance frameworks.