What backgrounds do your AI architects have?

Most are principal or distinguished engineers with 10+ years designing data and ML platforms at F500 scale, plus recent shipped GenAI stacks.

Which clouds and models do they cover?

Azure OpenAI, AWS Bedrock, Vertex AI, Databricks, Snowflake, plus open-weights via vLLM or SageMaker.

Contract, contract-to-hire or advisory?

All three — from a 4-week target-state sprint to embedded architects for the length of the program.

Do they define reference architectures and governance patterns?

Yes. Every architect produces target-state blueprints, model-routing standards, guardrail policies and FinOps guardrails aligned to your enterprise architecture board.

AI Strategy · Talent

Enterprise AI architects for production stacks.

Reference architects who design multi-model AI platforms across Azure OpenAI, AWS Bedrock and Vertex AI — with guardrails, data topology, model routing and cost controls mapped to your operating model.

48 hrs

3 vetted profiles delivered

F500

Prior enterprise-scale AI programs

2 weeks

Typical time-to-billable

Enterprise AI Architects podteam composition

Reference architect×1

Data / RAG architect×1

Security & governance×1

FinOps / cost lead×1

Certifications

Azure AIAWS BedrockVertex AITOGAFMLOps

Role-specific

Why teams book Talent with Pronix

48 hrs — 3 vetted profiles delivered
Reference architecture
Guardrails & governance
FinOps & cost controls

Book your working session

30 minutes, no slides. A senior delivery lead reviews your stack and gives you a concrete pilot outline.

Trusted by enterprise CX & digital teams

Azure OpenAI
AWS Bedrock
Vertex AI
Databricks
Snowflake

What you get

A working pilot in 90 days — not a 40-page slide deck.

Reference architecture

Target-state stacks across Azure OpenAI, Bedrock, Vertex AI and open-weights — model routing, fallback and residency mapped to your data topology.

Guardrails & governance

Trust Layer, Guardrails AI, prompt-injection defenses, PII redaction, evaluation gates and audit trails your risk team will sign off on.

FinOps & cost controls

Model selection matrices, caching strategies, token budgets and per-workload chargeback so AI spend stays predictable.

Book a 30-min working session →

Proof

“Their architect gave us a target-state stack our CIO, CISO and CFO all signed in one meeting. That never happens.”

Chief Architect — US bank

Multi-cloud AI reference architecture approved in 30 days

FAQ

Questions buyers ask us first.

What backgrounds do your AI architects have?: Most are principal or distinguished engineers with 10+ years designing data and ML platforms at F500 scale, plus recent shipped GenAI stacks.
Which clouds and models do they cover?: Azure OpenAI, AWS Bedrock, Vertex AI, Databricks, Snowflake, plus open-weights via vLLM or SageMaker.
Contract, contract-to-hire or advisory?: All three — from a 4-week target-state sprint to embedded architects for the length of the program.
Do they define reference architectures and governance patterns?: Yes. Every architect produces target-state blueprints, model-routing standards, guardrail policies and FinOps guardrails aligned to your enterprise architecture board.

Ready to see it in your stack?

30 minutes with a delivery lead. Your architecture, your KPIs, a concrete pilot outline you can defend internally.

Book a 30-min working session →