AI Solutions Architect

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What the market looks like

We've tracked 1,900+ specialist-level product postings in the last six months, with concentrations in technology, financial services, and professional services across California, New York, and Texas. AI Solutions Architects at this level typically command $170K–$320K in base compensation. The strongest candidates combine deep technical fluency in AI/ML systems with the ability to translate complex capabilities into product roadmaps and customer outcomes. Organizations are hiring for this role to bridge the gap between research, engineering, and go-to-market—someone who can evaluate AI feasibility, architect scalable solutions, and own the technical strategy behind product launches.

Job responsibilities

• Design and validate AI/ML solution architectures that align with product strategy, customer requirements, and organizational scalability constraints

• Partner with product management, engineering, and data science teams to translate AI capabilities into shipped features and customer-facing value

• Lead technical evaluations of new AI models, frameworks, and platforms; own the decision-making process for build-vs.-buy trade-offs

• Build and maintain reference implementations and proof-of-concept solutions that de-risk product roadmap items

• Drive technical design reviews and architecture governance; establish patterns and best practices for AI systems within the product organization

• Own integration of AI capabilities across the product surface; work backwards from customer problems to define technical requirements and success metrics

• Communicate solution trade-offs and technical implications to non-technical stakeholders, executives, and customers

Candidate requirements

• 5+ years of hands-on experience architecting and shipping AI/ML solutions in production environments

• Demonstrated expertise in model evaluation, system design patterns for AI inference, and productionization challenges (latency, cost, reliability, drift)

• Track record of translating business requirements into technical architecture decisions; comfort working across product, engineering, and business contexts

• Strong systems-level thinking: ability to evaluate trade-offs between accuracy, performance, maintainability, and operational cost

• Experience working in early-to-growth stage product teams or fast-moving environments where technical decisions directly impact shipping velocity

• Comfortable with ambiguity; ability to scope problems, define success metrics, and iterate toward solutions with incomplete information