Forward Deployed AI Architect

Responsibilities

• Embed with a client account, working directly with business owners, product leaders, engineering teams, security, risk, and operations to take AI solutions from pilot to production
• Design end-to-end AI-native architectures: orchestration, retrieval, integration with systems of record, agent autonomy and guardrails, identity and authorization, evaluation, observability, and cost model
• Own three artifacts that make your impact measurable: a capability economics model — what each AI capability costs and what it earns, so investment decisions are made on evidence; trust-rung evidence for every agent — the documented proof of how much autonomy each agent has earned and under what controls; a…
• Turn contradictory or incomplete requirements into explicit business decisions with named owners
• Decide, per component and per risk, how much can be delegated to AI — including AI coding agents in the delivery process — and where a human must stay in the loop
• Define how each system is evaluated: how the client will know an assistant or agent fits the business scope, not just that it produces fluent output
• Build proofs of concept and reference implementations when a decision needs evidence or a team needs an example of "good"
• Feed what you learn at the client back into EPAM's AI architecture practice: patterns, reference architectures, and what generation gets wrong

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