ML Cloud Engineer

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

• 35 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