Machine Learning Engineer

We are seeking, on behalf of our client, a highly skilled Machine Learning Engineer to design, build, and deploy end-to-end ML pipelines in a cloud-native environment. You will work closely with Data Engineers and Cloud/DevOps Engineers to operationalize models, ensuring they are scalable, observable, and seamlessly integrated into production systems. Responsibilities: • Design, implement, and maintain ML pipelines on GCP using tools like Vertex AI, Kubeflow, or MLflow. • Collaborate with Data Engineers to source, preprocess, and validate high-quality training data from BigQuery, PostgreSQL, and cloud-native storage. • Deploy, monitor, and optimize models in production environments, ensuring reliability, scalability, and cost efficiency. • Automate ML workflows with CI/CD pipelines and Infrastructure-as-Code (Terraform, ArgoCD). • Implement observability and monitoring for ML systems (drift detection, performance metrics, alerting). • Work with product and analytics teams to translate business problems into ML solutions. • Document processes, pipelines, and model governance for reproducibility and compliance. Requirements: • 3+ years of experience as an ML Engineer or similar role (MLOps, Data Science with strong engineering background). • Proficiency with Python and ML frameworks (TensorFlow, PyTorch, scikit-learn). • Experience with cloud-native ML platforms (Vertex AI, SageMaker, or Kubeflow). • Strong knowledge of data pipelines, feature stores, and model versioning. • Familiarity with CI/CD, Git, Terraform, and container orchestration (Kubernetes/GKE). • Understanding of observability for ML systems (logging, metrics, tracing, model drift). • Bonus: experience with real-time ML/streaming data (Kafka, Pub/Sub) or responsible AI practices. Contract Details: • Duration: 6 months (extendable based on project needs) • Location: Remote • Engagement: Contract