MLOPS Architect

Job Description

We are looking for an experienced

MLOps Architect

to design and implement scalable machine learning platforms and production-grade ML/AI solutions. The ideal candidate will have strong experience across

MLOps, cloud platforms, ML lifecycle management, CI/CD, automation, and Kubernetes

.

Key Responsibilities

• Design and architect scalable

MLOps platforms and ML/AI infrastructure

.

• Build and manage end-to-end

machine learning model lifecycle

from development through deployment and monitoring.

• Develop

CI/CD/CT pipelines

for ML models and data workflows.

• Implement model versioning, experiment tracking, model registry, and automated deployment processes.
• Design ML solutions using

AWS, Azure, or GCP

cloud platforms.

• Work with

Docker and Kubernetes

for containerized ML workloads.

• Implement model monitoring, performance tracking, drift detection, and production observability.
• Integrate data pipelines with ML training and inference workflows.
• Establish security, governance, scalability, and reliability standards for ML platforms.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, DevOps, and Architecture teams.
• Troubleshoot production ML systems and optimize infrastructure and deployment processes.

Required Skills

• 8+ years of experience in software/cloud/ML engineering, with strong MLOps experience.
• Strong hands-on experience with

MLOps architecture and ML lifecycle management

.

• Experience with

Python

and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

• Strong experience with

Docker, Kubernetes, and CI/CD

.

• Experience with

MLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI

.

• Strong knowledge of

AWS, Azure, or GCP

.

• Experience with Git, Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
• Knowledge of model monitoring, model governance, data/model versioning, and automated deployment.
• Strong understanding of APIs, microservices, cloud architecture, and infrastructure automation.
• Experience with

Terraform or similar Infrastructure-as-Code tools

is preferred.

Preferred

• Experience with Generative AI/LLM deployment and MLOps.
• Experience with RAG, model serving, vector databases, or AI platforms.
• Knowledge of cloud security and enterprise governance.
• Strong communication and stakeholder-management skills.