Senior GenAI Architect

Job title: Senior GenAI Architect

About the Role The Senior GenAI Architect designs, develops, and oversees the implementation of advanced Generative AI solutions, providing architectural leadership to deliver scalable, secure GenAI systems and drive client value. This role combines deep AI/ML expertise with enterprise architecture skills and requires collaboration across teams, mentorship of junior staff, and adherence to AI development and deployment best practices.

What You'll Do

• Architect GenAI Solutions: Design and develop robust architectures for generative AI applications tailored to business needs.
• Technical Leadership: Guide teams in model/framework/platform selection; ensure alignment with enterprise architecture standards.
• End-to-End Ownership: Oversee the full lifecycle from requirements gathering, model selection/training, deployment, monitoring, and optimization.
• Integration: Ensure seamless integration of GenAI solutions with existing systems, APIs, and data pipelines.
• Security & Compliance: Implement AI governance, data privacy, and security best practices; ensure compliance with regulations.
• Performance Optimization: Monitor and optimize model performance, scalability, and cost-efficiency.
• Research & Innovation: Stay abreast of GenAI advancements; evaluate emerging technologies and recommend adoption where appropriate.
• Mentorship: Provide technical guidance and mentorship to engineers, data scientists, and other stakeholders.
• Stakeholder Engagement: Communicate complex AI concepts and architectures to non-technical stakeholders; gather requirements and provide strategic recommendations.

What We're Looking For

• Bachelor’s or master’s degree in computer science, Engineering, Mathematics, or related field (PhD preferred).
• 15+ years of IT experience with 7+ years of experience in AI/ML and at least 2 years focused on generative AI.
• Proven experience architecting and deploying large-scale AI solutions in production environments.
• Proficiency in Python, deep learning frameworks, and cloud platforms.
• Strong understanding of data engineering, MLOps, and DevOps practices.
• Experience with prompt engineering, fine-tuning, and customizing foundation models.
• Familiarity with AI ethics, responsible AI principles, and regulatory compliance.
• Excellent problem-solving, communication, and leadership skills.