AI/ML/Gen AI Engineer

Experience Director - UX, UI & GenAI (12+ Years Experience)

"Responsibilities"

Lead strategic vision and execution for user experience design across digital products and AI-powered interfaces.

Drive integration of generative AI capabilities into user experiences, designing intuitive AI interaction patterns.

Establish UX/UI design systems, standards, and methodologies for traditional and AI-enhanced product experiences.

Lead multidisciplinary design teams including UX researchers, UI designers, and AI interaction specialists.

Collaborate with product leadership to define experience strategy and translate business objectives into user-centered design.

Pioneer design patterns for conversational AI, prompt engineering interfaces, and human-AI collaboration workflows.

Establish user research methodologies for AI product testing, including bias detection and ethical AI design practices.

Drive design innovation in emerging AI interfaces including voice, multimodal, and autonomous system interactions.

Partner with engineering teams to ensure technical feasibility and optimal implementation of AI-powered experiences.

Define metrics and KPIs for measuring AI product adoption, user satisfaction, and experience effectiveness.

"Required Qualifications"

12+ years of UX/UI design experience with 5+ years leading design teams and strategy.

Proven track record designing and shipping AI-powered products or features at scale.

Deep understanding of generative AI capabilities, limitations, and user interaction patterns.

Experience with design thinking methodologies, user research, and data-driven design decisions.

Strong leadership skills with experience managing design teams and cross-functional collaboration.

Master's degree in Design, HCI, Psychology, or equivalent experience in design leadership roles.

"Technical Skills"

Design Tools: Figma, Sketch, Adobe Creative Suite, Principle, Framer, InVision, Miro.

AI/UX Integration: Conversational UI design, prompt engineering UX, AI transparency patterns.

Research Methods: User interviews, usability testing, A/B testing, behavioral analytics, AI bias testing.

Prototyping: High-fidelity prototyping, interactive demos, AI mockup tools, voice interface prototyping.

Design Systems: Component libraries, design tokens, accessibility standards, cross-platform consistency.

Analytics: Google Analytics, Mixpanel, Hotjar, user behavior tracking, AI interaction metrics.

Collaboration: Cross-functional leadership, stakeholder management, design critique facilitation.

Emerging Tech: Voice UI, AR/VR interfaces, multimodal AI interactions, ethical AI design principles.