ML Cloud Engineer

4 hours ago

Bengaluru Karnataka India, Karnataka 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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