Job Description

We are seeking a skilled and proactive

MLOps Engineer

to join our AI/ML engineering team. The ideal candidate will have hands-on experience with

Kubernetes

,

FastAPI

,

Azure

, and

AWS

, and will be responsible for building, deploying, monitoring, and maintaining scalable machine learning models in production environments. You will work closely with Data Scientists, Backend Engineers, and DevOps teams to ensure robust and reliable ML pipelines and services.

Key Responsibilities:

Design, develop, and maintain CI/CD pipelines for ML model deployment. Containerize ML models using Docker and orchestrate them using Kubernetes. Build and expose RESTful APIs for ML services using FastAPI. Deploy and monitor ML workloads on cloud platforms (Azure and AWS). Implement and automate model versioning, monitoring, and logging solutions. Manage infrastructure as code using tools like Terraform or ARM templates. Optimize resource usage and scale services dynamically based on load. Ensure security, compliance, and high availability of ML services. Collaborate with cross-functional teams for data validation, model governance, and release workflows. Troubleshoot and resolve infrastructure or model deployment issues.

Required Skills and Experience:

3+ years of experience in MLOps or DevOps roles with a focus on machine learning. Strong hands-on experience with

Kubernetes (EKS/AKS)

. Proficient in building REST APIs with

FastAPI

or similar frameworks. Experience deploying ML models on

Azure ML

,

SageMaker

, or similar services. Familiarity with

Docker

,

GitHub Actions / Azure DevOps / Jenkins

. Cloud infrastructure experience in

Azure and AWS

(must have worked with both
Job Types: Full-time, Permanent

Pay: ?35,000.00 - ?50,000.00 per month

Work Location: In person