ML Cloud Engineer

1 day ago

Bengaluru, India TransPerfect Full time

OverviewWe are seeking a Mid-Level ML Dev / Cloud Engineer to support the development, deployment, and optimization of machine learning services in a cloud-native environment. This role focuses on building scalable pipelines, integrating models into production, and ensuring reliable cloud infrastructure for ML applications. The ideal candidate has hands-on experience with ML workflow tools, cloud orchestration, and software development best practices.Requirements3–5 years of hands-on experience in machine learning engineering, MLOps, or cloud engineering.Strong foundations in Python, ML workflows, and API development.Experience deploying models into production using Docker/Kubernetes.Practical experience with at least one major cloud provider (AWS, GCP, or Azure).Familiarity with ML lifecycle tools (MLflow, Airflow, Kubeflow, or similar).Experience building or maintaining CI/CD pipelines.Understanding of distributed systems, container orchestration, and cloud-native architectures.Ability to collaborate with data scientists, engineers, and stakeholders.Excellent problem-solving skills and comfort working in a fast-paced environment.ResponsibilitiesDevelop, maintain, and optimize ML pipelines, including data ingestion, preprocessing, feature engineering, and model deployment.Integrate machine learning models into production-grade APIs and services.Collaborate with data scientists to transition research models into scalable, cloud-ready solutions.Build automated workflows for model training, evaluation, monitoring, and CI/CD.Manage and optimize cloud infrastructure for compute, storage, orchestration, and networking.Implement model performance monitoring, logging, and automated alerting.Ensure reliability, scalability, and cost-efficiency of ML environments.Support containerization and microservices deployment using Docker/Kubernetes.Troubleshoot production ML workflows and resolve performance bottlenecks.Follow best practices for security, compliance, and version control within ML and cloud systems.Tech StackCloud Services (one or more):AWS: S3, SageMaker, Lambda, EC2, EKSGCP: GCS, Vertex AI, Cloud Run, GKEAzure: Blob Storage, ML Studio, AKSML / MLOps Tools:MLflow, Kubeflow, Airflow, TFX, SageMaker PipelinesModel serving frameworks: TensorFlow Serving, TorchServe, FastAPILanguages & Frameworks:Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow)Bash, SQLAPI development (FastAPI, Flask, Django)DevOps & Infra:Docker, KubernetesCI/CD tools (GitHub Actions, GitLab CI, Jenkins)Terraform or CloudFormation for IaCMonitoring: Prometheus, Grafana, CloudWatch

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