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Partner with business stakeholders and technical teams to define high-value AI and machine learning use cases and shape solution approaches that align with organizational objectives
Design enterprise architecture for intelligent systems spanning machine learning, generative AI, deep learning, virtual assistants, and cognitive technologies such as image and language processing
Produce target-state architecture artifacts and validate proposed designs through prototypes, pilot solutions, and minimum viable products
Review external platforms, tools, and vendor offerings in the AI/ML landscape and recommend options based on business fit, technical readiness, and long-term value
Contribute to strategic roadmaps covering AI, machine learning, advanced analytics, large language models, agent-based solutions, computer vision, data platforms, and event-driven processing
Stay current with industry advancements by continuously researching emerging practices, frameworks, and architectural patterns across AI and cloud-native technologies
Build proof-of-concept solutions quickly and independently to test models, algorithms, and technical approaches against real business challenges
Prepare and analyze data for experimentation by scripting data discovery, cleansing, transformation, and feature development activities across varied datasets
Support engineering and data science teams with architectural guidance for deploying, integrating, and operationalizing AI/ML capabilities within enterprise applications and workflows
Requirements
7+ years of experience in architecture, data architecture, or related enterprise technology roles
Demonstrated expertise in designing architecture for data-driven and AI-enabled solutions in complex business environments
Strong working knowledge of artificial intelligence, machine learning, and associated tools, platforms, and integration patterns
Ability to evaluate emerging technologies and apply them effectively to business problems with sound technical and commercial judgment
Excellent written, verbal, and presentation communication skills, with the ability to influence both technical and non-technical audiences
Proven ability to balance quality, cost, timing, and risk when shaping solution designs and architectural recommendations
Hands-on experience with prototyping, model evaluation, and technical validation of new tools, APIs, or frameworks
Strong interest in continuous learning and experimentation across AI/ML engineering, cloud-native solutions, and modern data platforms