AIML Architect

Qualification

Job Overview:

AI/ML Architect

Data Engineering

Design Thinking

Key Responsibilities:

• Lead the architecture and design of AI/ML solutions across multiple business domains.
• Collaborate with stakeholders to identify use cases and translate them into scalable, production-ready ML architectures.
• Leverage design thinking methodologies to drive innovative and user-centric solution design.
• Architect data pipelines and feature engineering processes for structured and unstructured data.
• Oversee the full ML lifecycle including data preprocessing, model training, evaluation, deployment, and monitoring.
• Ensure best practices in MLOps, including CI/CD for ML, model governance, and retraining strategies.
• Collaborate with data scientists, engineers, and product teams to align architecture with business goals.
• Mentor and guide engineering and data science teams on solution design, performance optimization, and system integration.
  

Role

Key Responsibilities:

• Lead the architecture and design of AI/ML solutions across multiple business domains.
• Collaborate with stakeholders to identify use cases and translate them into scalable, production-ready ML architectures.
• Leverage design thinking methodologies to drive innovative and user-centric solution design.
• Architect data pipelines and feature engineering processes for structured and unstructured data.
• Oversee the full ML lifecycle including data preprocessing, model training, evaluation, deployment, and monitoring.
• Ensure best practices in MLOps, including CI/CD for ML, model governance, and retraining strategies.
• Collaborate with data scientists, engineers, and product teams to align architecture with business goals.
• Mentor and guide engineering and data science teams on solution design, performance optimization, and system integration.
  

Experience

Job Reference Number