ML Cloud Engineer

16 hours ago

Bengaluru, India TransPerfect Full time

Overview We 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. Requirements 3–5 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. Responsibilities Develop, 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 Stack Cloud Services (one or more): AWS: S3, SageMaker, Lambda, EC2, EKS GCP: GCS, Vertex AI, Cloud Run, GKE Azure: Blob Storage, ML Studio, AKS ML / MLOps Tools: MLflow, Kubeflow, Airflow, TFX, SageMaker Pipelines Model serving frameworks: TensorFlow Serving, TorchServe, FastAPI Languages & Frameworks: Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow) Bash, SQL API development (FastAPI, Flask, Django) DevOps & Infra: Docker, Kubernetes CI/CD tools (GitHub Actions, GitLab CI, Jenkins) Terraform or CloudFormation for IaC Monitoring: Prometheus, Grafana, CloudWatch

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