<p><p><b>Role :</b> Machine Learning Engineer<br/><br/><b>Experience :</b> 610 Years<br/><br/><b>Location :</b> Bangalore, Noida, Pune (Onsite)<br/><br/><b>Type :</b> Full-time<br/><br/>We are seeking a highly skilled and collaborative Machine Learning Engineer with deep expertise in Google Cloud Platform (GCP) and Vertex AI to join our growing ML Engineering team.
You will play a critical role in designing, building, and deploying production-grade ML systems and pipelines that power key products and solutions.<br/><br/><b>Key Responsibilities :</b><br/><br/>- Design and develop scalable ML pipelines using Vertex AI Pipelines, Kubeflow, and Cloud Functions<br/><br/></p><p>- Build, train, tune, and deploy models using Vertex AI (AutoML and custom training jobs)<br/><br/></p><p>- Collaborate with Data Scientists to productionize research models using GCP tools like BigQuery, Dataflow, and Cloud Storage<br/><br/></p><p>- Ensure end-to-end ML lifecycle management : training, validation, deployment, versioning, and monitoring<br/><br/></p><p>- Apply MLOps best practices for CI/CD in ML (model registry, pipeline automation, reproducibility)<br/><br/></p><p>- Optimize model performance and cost across cloud services<br/><br/></p><p>- Work closely with cross-functional teams (Data Engineers, Product Managers, and DevOps) to deliver high-impact ML solutions<br/><br/><b>Required Skills :</b><br/><br/>- 610 years of experience in building and deploying machine learning solutions<br/><br/></p><p>- Strong hands-on experience with Google Cloud Platform (GCP) services, especially : Vertex AI (AutoML, Workbench, Pipelines, Model Registry), Cloud Functions, Cloud Storage, Dataflow</p><p><br/></p><p>- Solid programming skills in Python (with ML libraries like scikit-learn, TensorFlow, or PyTorch)<br/><br/></p><p>- Experience with CI/CD tools for ML (e.g., Cloud Build, GitHub Actions, MLflow)<br/><br/></p><p>- Good understanding of ML pipeline orchestration, monitoring, and retraining strategies<br/><br/></p><p>- Exposure to containerization (Docker) and orchestration (Kubernetes)<br/><br/><b>Preferred Skills :</b><br/><br/>- Experience with MLOps frameworks (e.g., TFX, Kubeflow)<br/><br/></p><p>- Experience with data versioning tools like DVC<br/><br/></p><p>- Exposure to multi-cloud environments or hybrid cloud setups<br/><br/></p><p>- Previous experience in large-scale enterprise ML systems</p><br/></p> (ref:hirist.tech)