ML Cloud Engineer

19 hours ago

bangalore, 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.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 StackCloud Services (one or more):- AWS: S3, SageMaker, Lambda, EC2, EKS- GCP: GCS, Vertex AI, Cloud Run, GKE- Azure: Blob Storage, ML Studio, AKSML / MLOps Tools:- MLflow, Kubeflow, Airflow, TFX, SageMaker Pipelines- Model serving frameworks: TensorFlow Serving, TorchServe, FastAPILanguages & 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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