AI/ML Architect

Key Responsibilities System Design:

Architects design the entire infrastructure for ML and LLM workflows, ensuring systems scale for high-volume, real-time inferences.

MLOps & Pipelines:

They implement continuous integration/continuous deployment (CI/CD) specifically for ML, integrating tools like Kubeflow, MLflow, and feature stores to automate training and deployment.

Model Governance & Evaluation:

Overseeing the training, fine-tuning, and evaluation of models to ensure accuracy, compliance, and risk management.

Cross-Functional Leadership:

They collaborate with data scientists, data engineers, and security teams to operationalize prototypes into viable business products.

Essential Skills & Tech Stack Cloud Platforms:

Deep expertise in deploying models using enterprise cloud infrastructure such as AWS SageMaker ,

Azure AI ML Frameworks:

Proficiency with frameworks like PyTorch, TensorFlow, and modern LLM orchestration tools for building agentic AI systems.

Data Engineering:

Mastery of data lakes, real-time data pipelines, and data governance.

Core Concepts:

Advanced knowledge of prompt engineering, fine-tuning strategies, and model monitoring to prevent data drift in production.