AIML Architect

Job Overview:

We are seeking a highly skilled AI/ML Architect with a strong foundation in Data Engineering and a proven ability to Design Thinking in solving complex business challenges. The ideal candidate will play a key role in shaping end-to-end AI/ML solutions—from data architecture to model deployment—while ensuring the design remains user-centric, scalable, and aligned with business objectives.

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.

Job Overview:

We are seeking a highly skilled AI/ML Architect with a strong foundation in Data Engineering and a proven ability to Design Thinking in solving complex business challenges. The ideal candidate will play a key role in shaping end-to-end AI/ML solutions—from data architecture to model deployment—while ensuring the design remains user-centric, scalable, and aligned with business objectives.

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.