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BearPlex AI architects design the production AI systems other engineers build: agent architectures, RAG pipelines, multi-tenant infrastructure, governance frameworks, the patterns that make AI work at enterprise scale. Senior technical leadership for engagements where architecture is the bottleneck.
What a AI Architect actually does at BearPlex
An AI architect at BearPlex owns the architecture of production AI systems: making the design decisions that shape how systems work, scale, and evolve over months and years. The role spans: agent architecture (single-agent vs multi-agent, state management, tool design, HITL patterns), RAG architecture (retrieval pipelines, chunking strategy, multi-tenancy, evaluation infrastructure), data architecture for AI (feature stores, embedding pipelines, training data infrastructure), governance architecture (model registry, audit logging, compliance integration), and the platform architecture that supports all the above. Our architects are senior engineers (typically 10+ years) who've shipped production AI systems and now design them at scale. They produce: architecture documents that survive engineering team scrutiny, design reviews that catch problems before they're built, and the patterns that get implemented across many AI projects within an organization. They also know when NOT to architect: when a simple solution beats a sophisticated one, when the right answer is to use someone else's product instead of building, when premature abstraction will slow the team down.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Architected the production AI platform for a Series C SaaS: supports 14 AI features across 800+ customers; designed multi-tenancy that hasn't had a single cross-tenant incident.
Designed enterprise AI architecture for a top-20 US bank: examiner-defensible, supports 12 production initiatives, passed first-line MRM review on first submission.
Architected a multi-agent system for an autonomous research product: production for 18 months, handles 50K+ daily research queries with full observability and cost control.
Designed AI architecture for a healthcare imaging startup: passed FDA SaMD review, currently deployed across 8 hospital networks with HIPAA-compliant audit trail.
Skills matrix
The capabilities every BearPlex AI Architect brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Production agent architecture (single + multi-agent) | Expert | LangGraph · Claude Agent SDK · explicit state design · HITL patterns |
| RAG architecture (chunking, retrieval, reranking, eval) | Expert | LlamaIndex · Pinecone / Qdrant / pgvector · Cohere Rerank · hybrid search design |
| Multi-tenant SaaS AI architecture | Expert | per-tenant isolation patterns · IAM design · tenant-scoped retrieval |
| Sovereign / on-prem deployment architecture | Expert | VPC design · vLLM serving · BAA-compliant cloud · air-gapped patterns |
| Evaluation harness architecture | Expert | Promptfoo · Braintrust · LLM-as-judge design · regression testing infrastructure |
| Model governance architecture (OCC 2011-12, NIST AI RMF) | Expert | Model registry design · audit logging architecture · MRM integration |
| Cost optimization architecture | Expert | Prompt caching · model routing · distillation pipelines · batch processing |
| Observability and monitoring architecture | Expert | LangSmith · OpenTelemetry · production trace analysis |
| Build-vs-buy decision frameworks | Expert | TCO modeling · vendor evaluation · strategic analysis |
| Architecture review and design critique | Expert | Design review processes · ADR templates · pattern libraries |
| Cross-functional architecture (product + engineering + compliance) | Expert | Stakeholder alignment · constraint mapping |
| Documentation and knowledge transfer | Expert | Architecture decision records · design system documentation · team training |
How we vet AI architects
Senior architecture interview
90-minute deep-dive on past architecture work. We probe: what design decisions did the architect make that aged well? What designs failed in production and what did they learn? Can they defend trade-offs under scrutiny? We screen out architects whose work has never shipped to production at scale.
Live architecture exercise
We give the architect a complex realistic problem (multi-tenant agent system, healthcare RAG with HIPAA constraints, fintech AI with MRM requirements) and 2 hours. They must produce an architecture with explicit trade-off rationale, design reviews of two alternative approaches, and risk assessment.
Reference deep-dive on shipped architectures
Three reference checks focused on architectures that actually shipped to production, and how they performed 6+ months in. Architects who designed beautiful systems that fell apart in production are a red flag; architects whose designs aged well are the green flag.
Hamad-led trial engagement
Trial engagement on a real client architecture problem, supervised directly by Hamad Pervaiz. Architecture quality requires senior judgment that's hard to test in interview; the trial proves it.
What clients say
“Their architect saved us from building the wrong system. We were 6 weeks into design for a multi-agent platform; he came in for a 1-week review and showed why the architecture wouldn't survive production. We restarted on a sound foundation and shipped 3 months earlier than the original plan.”
“We needed enterprise AI architecture that would pass examiner review. The BearPlex architect designed the platform that's now serving 12 production AI initiatives across our bank: first-line and second-line MRM both signed off without major findings.”
“Their architect's instinct for what NOT to build was as valuable as what to build. He killed three planned features in our first design review with rigorous reasoning: we shipped a tighter product that customers loved.”
Hiring AI architects: questions answered
Most do, though it's not the primary deliverable. Our architects came up through senior engineering roles and continue to ship code on engagements where the team is small. For larger engagements with dedicated engineering teams, the architect's role is design and review rather than implementation.
Hire an architect when: (1) you have multiple AI initiatives that need consistent patterns, (2) the architecture decisions are high-stakes (multi-tenant, regulatory, sovereign), (3) you have engineers who can implement but need senior design leadership, (4) you're at the inflection point where ad-hoc decisions stop working. Hire engineers when: implementation is the bottleneck, the architecture is well-understood, or you need execution capacity.
Yes: common engagement model. The architect works as part of your team for 3-12 months, designs and shepherds the architecture, mentors junior engineers, and hands off ownership at the end. The goal is your team owning the architecture after we leave, not permanent vendor dependency.
Yes: common engagement type. 1-3 week intensive review of an existing architecture, with written recommendations and design improvement plan. Particularly valuable before major scaling moments (Series B onward) or when teams suspect they're heading toward problems.
Architecture review: $40K-$120K depending on scope (1-3 weeks). Embedded architect: $40K-$60K monthly retainer (3-12 months). New-system architecture engagement: $80K-$300K (4-12 weeks producing complete architecture deliverables). We bill on outcomes; we'd rather do focused high-impact work than long-running advisory.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async handoff for the rest. For US-based engagements requiring more synchronous work, we have architects in PST / EST time zones.
Yes: our architects are generalists with deep expertise in 2-3 specific areas. Most have experience across RAG, agent systems, and at least one of fine-tuning / MLOps / multi-tenant SaaS. For highly specialized architecture work (e.g., low-latency trading AI, FDA-regulated medical AI), we staff architects with direct domain experience.
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