Applied AI Specialist

What You'll Do

Lead AI Discovery Workshops - Partner with strategic ecosystem providers, including NVIDIA, to design and facilitate highly technical hands-on workshops that identify, validate, and prioritize private AI opportunities.

Discover and Scope High-Value Use Cases - Conduct deep technical and business discovery with enterprise customers to identify AI workloads that can deliver measurable business outcomes and are suitable for production deployment.

Evaluate Enterprise AI Readiness - Assess data architecture, governance requirements, infrastructure constraints, security considerations, and data gravity challenges to determine the feasibility of Private AI deployments.

Develop Regional Domain Leadership - Serve as a subject matter expert within your regional pod, building deep expertise in one or more strategic industries while helping transfer successful AI patterns across customer segments.

Build Technical Assets - Develop lightweight proofs of concept, reference architectures, technical demonstrations, and solution frameworks that help customers validate feasibility before committing to full-scale implementation.

Define the Engineering Blueprint - Translate qualified opportunities into clear technical requirements, data specifications, success criteria, architectural recommendations, and scoped outcomes to ensure seamless handoff to FDE teams.

Capture and Scale Best Practices - Document successful use cases, architectural patterns, and implementation frameworks that can be reused across customers, industries, and regions.

What We're Looking For

The Builder Mindset - Strong technical foundation in data science. Comfortable working directly with code, APIs, notebooks, development frameworks, and enterprise architectures.

Hands-On AI Experience - Practical experience building or supporting AI applications across machine learning, deep learning, computer vision, time-series analytics, predictive modeling, anomaly detection, and generative AI. Comfortable discussing model architectures, training and evaluation methodologies, inference patterns, and production deployment considerations across a broad range of enterprise AI workloads.

Enterprise Discovery and Solutioning Experience - Proven success in technical pre-sales, solution architecture, consulting, customer engineering, or similar roles involving the discovery, qualification, and design of complex enterprise technology initiatives.

Data Readiness Assessment Skills - Ability to evaluate data quality, governance, security, compliance, operational readiness, and infrastructure constraints that impact successful private AI deployments.

Executive Communication Skills - Exceptional ability to engage technical and executive audiences, facilitating conversations that connect business outcomes with practical implementation strategies.

Technical Proficiency - Experience with Python, APIs, cloud-native architectures, data pipelines, MLOps, and modern AI development ecosystems.

Preferred Qualifications

Experience working with NVIDIA AI Enterprise, NIMs, NeMo, RAPIDs or accelerated computing platforms.

Experience delivering customer-facing workshops, design sessions, or architecture engagements.

Experience moving AI projects from proof of concept into production environments.

Familiarity with enterprise data platforms, governance frameworks, and hybrid cloud architectures.

This role is not eligible for immigration sponsorship.

The anticipated annual base salary range for this position is:
Washington: $171,000 - $200,000

Colorado: $171,000 - $200,000

What We Offer

Centralized Platform Support - You will never lose time troubleshooting cloud credentials, environment setup, or workshop preparation. A dedicated Global Workshop Platform Engineer builds, seeds, manages, and tears down workshop environments programmatically, allowing you to focus entirely on customer engagement and solution discovery.

Clean Operational Boundaries - Your mission is discovery, validation, and qualification. Once a use case has achieved technical conviction and is approved for execution, a dedicated FDE Pod assumes ownership of implementation and production delivery. This allows you to remain focused on uncovering the next strategic opportunity.

Direct Access to Industry Innovation - You will work alongside leading AI practitioners, strategic ecosystem partners, and enterprise innovators to help shape how AI is deployed across some of the world's largest organizations.

High-Impact Culture - You will be part of a highly visible, practitioner-led organization with executive sponsorship and a clear mandate to drive measurable business outcomes through AI.

Career Growth - As the Applied AI organization expands globally, you will have opportunities to deepen industry expertise, influence go-to-market strategy, mentor future practitioners, and help define the operating model for one of the company's most strategic growth initiatives.

What you can expect from us:

Generous PTO Policy

Support work life balance with Unplugged Days

Flexible WFH Policy

Mental & Physical Wellness programs

Phone and Internet Reimbursement program

Access to Continued Career Development

Comprehensive Benefits and Competitive Packages

Paid Volunteer Time

Employee Resource Groups

Skills Required
Strong technical foundation in data science (comfortable with code, APIs, notebooks, development frameworks, enterprise architectures)
Hands-on experience building or supporting AI applications (machine learning, deep learning, computer vision, time-series analytics, predictive modeling, anomaly detection, generative AI)
Experience with Python
Experience with APIs and notebooks (e.g., Jupyter)
Experience with cloud-native architectures, data pipelines, and MLOps
Proven success in technical pre-sales, solution architecture, consulting, customer engineering, or similar roles
Ability to evaluate data quality, governance, security, compliance, and infrastructure constraints for AI deployments
Exceptional executive communication skills
Experience with NVIDIA AI Enterprise, NIMs, NeMo, RAPIDS, or accelerated computing platforms
Experience delivering customer-facing workshops, design sessions, or architecture engagements
Experience moving AI projects from proof of concept into production environments
Familiarity with enterprise data platforms, governance frameworks, and hybrid cloud architectures