AI Solution Architect

Shelf

New York (NY)

On-site

USD 140,000 - 180,000

Full time

14 days+

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Benefits offered by this job

Competitive salary

Equity

Comprehensive health and wellness packages

Job summary

Shelf is seeking an AI Solution Architect who will be responsible for designing concrete, deployable architectures in customer-facing engagements. The role requires deep experience in AI systems, enterprise architecture, and effective communication with senior stakeholders.

This position is NYC-based and entails leading pre-sales technical discussions, guiding pilot phases, and collaborating closely with customers and the engineering team.

The ideal candidate will have 8-12+ years of relevant experience and expertise in data modeling and architecture.

Qualifications

8‑12+ years in solutions architecture, technical pre‑sales, or data/AI engineering in customer‑facing roles.
Strong expertise in data modeling, data platforms, and ETL/data pipelines.
Experience with knowledge graphs, ontologies, or semantic technologies strongly preferred.

Responsibilities

Lead pre‑sales technical conversations on architecture and deployment strategy.
Design concrete target architectures from customer data architectures.
Guide proof‑of‑value pilots and collaborate with Engineering.

Skills

Solution Architecture Expertise

Enterprise Technical Authority

Data & Ontology Modeling

Senior Stakeholder Engagement

Customer‑Facing Excellence

Collaborative Delivery

Sales‑Oriented Mindset

Continuous Learner

Education

Bachelor’s degree in Computer Science, Engineering, Data Science, or related field

Tools

Python

AWS

Azure

GCP

Job description

*noh1b,OPT,visasponsorshipwillbeprovidedoraccepted*

About Shelf:

We're building AI that is changing the world. The workforce of the future is going to be multi-dimensional, multi-faceted, conductors of both people and artificial intelligence. We're looking for someone who can do that.

Shelf is the OS for agentic AI: a platform that models your policies, workflows, and operational logic into an AI Data Model so that AI agents don’t just respond, they reason. The result? AI that understands how your business actually runs and delivers precise, compliant, auditable outcomes at scale.

Brands like Amazon, Nespresso, HelloFresh, and KeyBank use Shelf to power their AI agents that automate workflows, improve efficiency, and turn long processes into automated work. We’re partnered with Microsoft, Salesforce, OpenAI, Snowflake, and Databricks — and recognized by Gartner (Cool Vendor) and IDC (Innovator) for our approach.

Job Description:

The AI Solution Architect is Shelf's solution design authority in customer-facing engagements, translating complex enterprise data and GenAI challenges into concrete, deployable architectures. This role combines deep expertise in AI systems, enterprise architecture, and data modeling with the ability to engage senior technical stakeholders and design end-to-end solutions that prove measurable value and scale into production.

You’ll engage early in the sales cycle, leading pre‑sales technical conversations on architecture, security, and deployment strategy with senior prospect and customer stakeholders. Operating at a high level, you’ll run technical discovery, design the target solution, and guide proof‑of‑value pilots that demonstrate Shelf’s value on a prospect’s real data, working directly alongside Engineering throughout. Beyond the initial win, you’ll define the reference architecture that guides production rollout and expansion.

Reporting to the Field CTO, you’ll partner with Account Executives throughout the sales cycle, with primary responsibility during technical evaluation and pilots. You’ll collaborate with Customer Success through deal closing, onboarding, and initial enablement, and contribute to our land‑and‑expand growth strategy by architecting solutions that grow within existing accounts. Your insights from the field will feed directly into product direction through structured feedback to our CTO and Product leadership.

The ideal candidate brings 8‑12+ years combining deep technical depth in data and AI platforms with customer‑facing solution design. You’re equally comfortable whiteboarding a target architecture with a CTO and getting into the detail of a pilot alongside Engineering. You communicate credibly at a senior level while maintaining the technical depth to go deep when it counts. Experience with knowledge graphs, ontologies, or semantic technologies is strongly preferred, though exceptional data modeling and architecture expertise can substitute.

What We’re Looking For:

Solution Architecture Expertise - Designs end-to-end technical solutions and reference architectures; translates ambiguous enterprise requirements into clear, deployable designs
Enterprise Technical Authority - Strong, current command of GenAI, LLMs, RAG, and agent‑based systems, combined with enterprise architecture, security, and deployment strategy; credible with senior technical stakeholders
Data & Ontology Modeling - Strong data modeling, data platforms, and ETL/pipeline expertise; knowledge graphs and ontologies experience strongly preferred
Senior Stakeholder Engagement - Comfortable leading high-level conversations with senior prospect and customer stakeholders such as Enterprise Architects, Data Leaders, CTOs, and AI Leaders; translates technical concepts into business value
Customer‑Facing Excellence - Exceptional communication and presentation skills; comfortable whiteboarding architectures and guiding technical evaluations
Collaborative Delivery - Works directly alongside Engineering during pilots to prove value on real customer data
Sales‑Oriented Mindset - Understands enterprise sales cycles and is motivated by technical wins, conversions, and expanding existing accounts
Continuous Learner - Stays current with AI/GenAI trends, data technologies, and modern engineering practices

Key Responsibilities:

Lead pre‑sales technical conversations on architecture, security, and deployment strategy with senior prospect and customer stakeholders
Lead solution design and technical discovery, translating customer data architectures, GenAI initiatives, and integration requirements into concrete target architectures
Design and guide proof‑of‑value pilots that prove measurable value on customer data, working directly alongside Engineering
Define reference architectures and solution designs that guide production rollout and scale within accounts
Present solutions to Enterprise Architects, Data Leaders, AI/ML Engineers, and senior technical decision‑makers, translating technical capabilities into business value
Support Account Executives throughout sales cycles with primary ownership during technical evaluation and pilot phases
Collaborate with Customer Success through deal closing, onboarding, and initial enablement
Support existing customer expansion opportunities as part of our land‑and‑expand strategy
Build reusable ontology templates, reference architectures, and solution best practices in collaboration with the Field CTO and Engineering
Provide structured field feedback to CTO and Product leadership to influence roadmap and strategic direction

Qualifications

8‑12+ years in solutions architecture, technical pre‑sales, enterprise architecture, or data/AI engineering in customer‑facing roles
Strong solution design and enterprise architecture experience, including security and deployment considerations
Strong expertise in data modeling, data platforms, and ETL/data pipelines
Experience with knowledge graphs, ontologies, or semantic technologies strongly preferred
Strong, current command of AI/GenAI technologies (LLMs, RAG, agent‑based systems)
Deep understanding of enterprise data platforms and cloud architectures (AWS, Azure, GCP)
Technical enough to be hands‑on when needed; familiarity with scripting (Python) and modern AI coding tools (Claude Code, Cursor, etc.) a plus
Proven track record engaging senior technical stakeholders in customer‑facing roles with demonstrated sales impact
Exceptional presentation and communication skills with technical and executive audiences
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field
Experience in B2B SaaS, AI/ML, or data quality solutions preferred