Principal Data & AI Architect

About the role

The Principal Data & AI Architect will design, develop, and implement advanced Data & AI solutions and architectures that align with the company’s strategic goals. This individual contributor role requires deep technical expertise, leadership, and collaboration across cross-functional teams to deliver scalable and innovative AI solutions.

Responsibilities

• Lead the design and development of Data & AI architectures, including Enterprise data/MDM solutions, machine learning models, deep learning frameworks, and generative AI systems.
• Analyze, implement, and deploy solutions both on-premises and in the cloud.
• Define technical strategies and roadmaps for Data & AI-driven projects, ensuring alignment with business objectives.
• Collaborate with data scientists, data engineers, business, and product teams to integrate Data & AI solutions into production environments.
• Advise and oversee the evaluation and adoption of Data & AI technologies, tools, and platforms.
• Serve as the technical leader and mentor to Data & AI and engineering teams.
• Deliver scalable, secure, and optimized Data & AI solutions.
• Lead Data and analytic literacy within the organization and translate deep technical concepts into simple business vernacular.
• Monitor and lead industry trends and advancements in Data & AI to maintain a competitive edge.
• Communicate complex technical concepts to non-technical stakeholders effectively.

Requirements

• Advanced degree (Master’s or Ph.D.) in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 8+ years of experience in AI, machine learning, or data science, with at least 4 years in a senior or lead architect role.
• Proven track record of designing and deploying large-scale Data & AI systems in production environments.
• Experience leading cross-functional teams in the delivery of complex AI projects.
• Hands-on experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and AI frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Deep curiosity and a desire to experiment.
• Expertise in machine learning algorithms, neural networks, genetic algorithms, decision trees, business dynamic models, agent-based models, advanced statistical techniques, and operations research.
• Strong proficiency in programming languages such as Python, R, or Java.
• Ability to design scalable, secure, and efficient AI architectures.
• Exceptional problem-solving and analytical skills.
• Strong leadership and mentorship abilities, with a focus on fostering innovation and collaboration.
• Excellent communication skills, capable of translating technical concepts to diverse audiences.
• Ability to work in a fast-paced, dynamic environment and manage multiple priorities.

Preferred Qualifications

• Ph.D. in Operations Research, Data Science, or a closely related field.
• Master’s degree in a relevant field with significant research or project work in AI or machine learning.
• Relevant certifications such as AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer, or Microsoft Certified: Azure AI Engineer Associate.
• Experience with generative AI and reinforcement learning.
• Publications or patents in AI, machine learning, or related fields.
• Familiarity with DevOps practices and MLOps pipelines for AI deployment.
• Experience in industries such as healthcare, finance, or technology.

Physical Requirements and Working Conditions

• Regularly required to talk, hear, and use hands to manipulate objects or controls.
• Regularly required to stand and walk.
• Occasionally required to stoop, bend, or reach above the shoulders.
• Must occasionally lift up to 25 pounds.
• Typical conditions involve frequent and continuous computer-based work requiring periods of sitting, close vision, and the ability to adjust focus.
• Occasional travel.