AI Cloud Engineer

Role Overview :

We are looking for a skilled and forward-thinking AI Cloud Engineer to join our AI & Cloud Engineering team. This role is ideal for someone who thrives at the intersection of cloud infrastructure, machine learning, and AI model deployment.

You will play a key role in architecting, building, and maintaining scalable, secure, and high-performance AI / ML solutions on leading cloud platforms like Azure, AWS, or Google Cloud.

The ideal candidate should have strong experience in cloud-native development, MLOps, infrastructure as code (IaC), and deploying AI / ML models at scale.

Key Responsibilities :

Cloud Architecture & Engineering :

• Design, implement, and maintain cloud-based infrastructure to support AI / ML workloads.
• Architect and deploy solutions using Azure AI, AWS SageMaker, or Google Vertex AI.
• Leverage services such as Kubernetes, Docker, Blob Storage, Serverless Functions, and API Gateways to build scalable AI & ML Deployment :
• Deploy, monitor, and optimize machine learning models and large language models (LLMs) in production environments.
• Develop APIs or microservices for AI model inference and integrate them into applications.
• Implement MLOps practices for model versioning, continuous integration, continuous delivery (CI / CD), and Engineering & Pipeline Management :
• Design and manage data pipelines for model training and inference using tools like Apache Airflow, Azure Data Factory, or Glue.
• Ingest, transform, and process structured and unstructured data in cloud & Compliance :
• Ensure data security, privacy, and compliance across cloud services and AI applications.
• Implement role-based access control (RBAC), secure APIs, and encryption in transit and at & DevOps :
• Use IaC tools like Terraform, Bicep, or CloudFormation to automate cloud infrastructure.
• Build and manage CI / CD pipelines for model deployment using Azure DevOps, GitHub Actions, or & Qualifications :

Technical Skills :

1. Python for AI / ML workloads

2. MLOps tools : MLflow, Kubeflow, SageMaker Pipelines, Azure ML

3. Containers and orchestration : Docker, Kubernetes

4. CI / CD : Azure DevOps, GitHub Actions, GitLab CI

5. Infrastructure as Code (IaC) : Terraform, ARM / Bicep, CloudFormation

(ref : hirist.tech)