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 pipelines.

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 monitoring.

Data 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 environments.

Security & 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 rest.

Automation & 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 Jenkins.

Skills & Qualifications :

Technical Skills :

- Proficiency in one or more cloud platforms : Azure, AWS, or Google Cloud Platform

- Strong experience with :

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

- Familiarity with AI frameworks : TensorFlow, PyTorch, Hugging Face Transformers

- Good knowledge of API development, microservices, and REST/GraphQL APIs