The AI Architect is responsible for designing and overseeing the end-to-end architecture of
complex Artificial Intelligence and Machine Learning solutions. This role bridges the gap
between high-level business requirements and the technical implementation of scalable,
reliable, and secure AI systems.
Key Responsibilities
● Architectural Blueprint: Design the complete technical stack for AI solutions, including
data acquisition, processing pipelines, model training environments, and
deployment/inference services.
● Technology Evaluation: Evaluate and select appropriate AI/ML frameworks, cloud
services (AWS SageMaker, Azure ML, GCP Vertex AI), and infrastructure components
to meet performance needs.
● MLOps Strategy: Define MLOps practices, including continuous integration and
continuous deployment (CI/CD) for models, monitoring model drift, and ensuring
reproducibility.
● Security and Governance: Ensure all AI systems adhere to security protocols, data
privacy regulations, and ethical AI guidelines.
● Team Leadership: Guide and mentor Data Scientists and ML Engineers on
architectural decisions, performance tuning, and best coding practices.
Required Qualifications
● Master’s degree or Ph.D. in Computer Science, Data Science, or a related technical
discipline is preferred.
● 8+ years of experience in software development or data engineering, with 3+ years
specifically as an ML or AI Architect.
● Deep expertise in a major cloud AI platform (AWS, Azure, or GCP) and related services.
● Expertise in machine learning algorithms, deep learning frameworks (e.g., TensorFlow,
PyTorch), and model deployment methodologies.
Required Qualifications
● Master’s degree or Ph.D. in Computer Science, Data Science, or a related technical
discipline is preferred.
● 8+ years of experience in software development or data engineering, with 3+ years
specifically as an ML or AI Architect.
● Deep expertise in a major cloud AI platform (AWS, Azure, or GCP) and related services.
● Expertise in machine learning algorithms, deep learning frameworks (e.g., TensorFlow,
PyTorch), and model deployment methodologies.