Machine Learning Engineer

3 weeks ago


Noida, India QUARKS TECHNOSOFT PRIVATE LIMITED Full time

Role : Machine Learning EngineerExperience : 610 YearsLocation : Bangalore, Noida, Pune (Onsite)Type : Full-timeWe 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.Key Responsibilities :- Design and develop scalable ML pipelines using Vertex AI Pipelines, Kubeflow, and Cloud Functions- Build, train, tune, and deploy models using Vertex AI (AutoML and custom training jobs)- Collaborate with Data Scientists to productionize research models using GCP tools like BigQuery, Dataflow, and Cloud Storage- Ensure end-to-end ML lifecycle management : training, validation, deployment, versioning, and monitoring- Apply MLOps best practices for CI/CD in ML (model registry, pipeline automation, reproducibility)- Optimize model performance and cost across cloud services- Work closely with cross-functional teams (Data Engineers, Product Managers, and DevOps) to deliver high-impact ML solutionsRequired Skills :- 610 years of experience in building and deploying machine learning solutions- Strong hands-on experience with Google Cloud Platform (GCP) services, especially : Vertex AI (AutoML, Workbench, Pipelines, Model Registry), Cloud Functions, Cloud Storage, Dataflow- Solid programming skills in Python (with ML libraries like scikit-learn, TensorFlow, or PyTorch)- Experience with CI/CD tools for ML (e.g., Cloud Build, GitHub Actions, MLflow)- Good understanding of ML pipeline orchestration, monitoring, and retraining strategies- Exposure to containerization (Docker) and orchestration (Kubernetes)Preferred Skills :- Experience with MLOps frameworks (e.g., TFX, Kubeflow)- Experience with data versioning tools like DVC- Exposure to multi-cloud environments or hybrid cloud setups- Previous experience in large-scale enterprise ML systems (ref:hirist.tech)



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