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

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