AI Solutions Architect

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What the market looks like

AI solutions architecture has been one of the fastest-growing areas we track — over 11,000 live postings in the past year — and the role now sits at the center of consultancies, systems integrators, and enterprise architecture functions. Mid-senior total compensation generally lands in a $150K–$260K band, with director-level roles clearing $280K. The strongest architects we place combine modern GenAI fluency with the patience to make pragmatic build-versus-buy calls under real constraints — cost, latency, governance, and team capability all matter.

Job responsibilities

• Design end-to-end AI solutions for enterprise clients across industries, from data layer up through application

• Translate business problems into technical architectures spanning data, modeling, orchestration, and evaluation

• Lead technical conversations with senior stakeholders on both client and delivery teams

• Define reference architectures, patterns, and reusable assets that raise the bar across the broader practice

• Evaluate and select technologies — foundation models, vector databases, orchestration frameworks — for specific client contexts

• Partner with engineering leads to drive delivery quality, not just technical correctness

• Mentor senior engineers and participate in the pre-sales process where it helps win the right work

• Stay current with the AI landscape and bring that perspective back into client conversations without chasing every trend

Candidate requirements

• 7+ years of technical architecture or senior engineering experience, with 2+ years in AI or ML delivery

• Strong fluency across modern AI stacks — RAG, agentic workflows, MLOps, and major cloud platforms

• Track record of shipping AI solutions end-to-end in enterprise environments, not just prototypes

• Excellent communication — equally comfortable with CIOs, engineers, and end users

• Consulting, professional services, or internal enterprise architecture background preferred

• Pragmatism about when not to use AI — cost, latency, and governance trade-offs matter