Machine Learning Engineer

Experience- 5 to 10 years

Location- HYD

Work timings- 2 PM to 11 PM IST

Working from client – all 5 days

DBS/DBS-/2025/2778559

We are looking for a skilled MLOps Engineer to join our team and help us build, deploy, and maintain robust and scalable machine learning systems. You will be responsible for the full lifecycle of our ML pipelines, from data ingestion to model serving. This is a hands-on role where you will design and implement automated workflows, ensure data quality, and manage model deployments in a production environment.

Responsibilities

• Data and Feature Pipelines: Design, build, and manage automated data ingestion, transformation, and validation pipelines using services like Kubeflow Pipelines and Vertex AI Pipelines .
• Feature Engineering: Implement and containerize feature engineering logic for diverse datasets, ensuring reusability and scalability.
• Data Validation: Integrate and manage data validation processes, including leveraging advanced techniques like AI Agents and the Generative Language API to automatically detect and remediate data quality issues.
• Model Training and Experimentation:
• Set up and maintain automated continuous training ( CT ) pipelines using Vertex AI Pipelines (Schedules) and Cloud Scheduler .
• Implement experiment tracking to log and compare model parameters, metrics, and artifacts.
• Configure and execute Hyperparameter Tuning jobs using Vertex AI Training to optimize model performance.
• Model Management: Establish a robust Model Versioning system to manage and store model artifacts securely in a centralized repository ( Cloud Storage ).
• Deployment and Serving:
• Containerize ML models and their dependencies using Docker and manage images with Artifact Registry .
• Build and maintain CI/CD workflows for ML models, ensuring seamless and automated deployment.
• Configure and manage low-latency production serving environments using Vertex AI Endpoints for real-time inference.