Forward Deployed AI Architect

Salary: $65,000 - 102,000 per year Requirements:

• We expect you to have taken AI-native systems to production for a client or customer, such as RAG, assistants, agents, or agentic workflows with real users and real business processes, in a solutions architecture, customer engineering, forward-deployed, or consulting role with architect-level responsibility for the surrounding system.
• We are looking for a proven solution architect from a consulting or client-facing background who has recently started building with AI independently and wants to make AI-native design at the client side the focus of our work.
• You should have a history of client- or customer-facing architecture work, including framing problems for non-technical stakeholders, presenting options with costs and risks, and driving decisions.
• We need solid solution or software architecture experience with delivery accountability, meaning the systems you designed were shipped and operated successfully.
• You should bring deep experience in integration, distributed systems, cloud, and enterprise-scale data-flow architecture.
• You must be comfortable working through ambiguity, conflicting stakeholders, and changing requirements without waiting for perfect clarity before beginning.
• We value a consistent habit of documenting decisions so they remain usable over time, such as ADRs, design documents, and evidence for autonomy and risk decisions.
• You should have recent hands-on coding ability, enough to prototype and keep constraints realistic.
• You must be willing to work on-site with clients and travel as the engagement requires.
• Nice-to-have experience includes regulated industries such as financial services, healthcare and life sciences, or insurance, especially working with security, risk, and compliance teams.
• Additional advantageous experience includes agent frameworks, tool and function calling, MCP, orchestration patterns, LLM observability, identity and authorization for agentic systems, cost and token economics, presales or proposal work, account shaping, and forward-deployed or field engineering experience at an AI or data platform vendor.

• We embed with a client account and work directly with business owners, product leaders, engineering teams, security, risk, and operations to move AI solutions from pilot to production.
• We design end-to-end AI-native architectures covering orchestration, retrieval, integration with systems of record, agent autonomy and guardrails, identity and authorization, evaluation, observability, and cost modeling.
• We own three core artifacts that make our impact measurable: a capability economics model, trust-rung evidence for each agent, and a knowledge-graph slice of the client domain.
• We turn contradictory or incomplete requirements into explicit business decisions with named owners.
• We determine, per component and per risk, how much authority can be delegated to AI, including AI coding agents in the delivery process, and where human oversight must remain.
• We define how each system is evaluated so the client can see whether an assistant or agent truly fits the business scope.
• We build proofs of concept and reference implementations whenever a decision needs evidence or a team needs a concrete example of strong practice.
• We feed lessons from the client back into our AI architecture practice through patterns, reference architectures, and insight into where the next generation falls short.

• AI
• Architect
• Cloud
• Flow
• LLM
• MCP
• RAG
• Security
• Embedded
• OAuth

More:

Most enterprise AI efforts get stuck between pilot and production, and we place the Forward Deployed AI Architect inside the client to help remove that blocker. This role sits at the architecture level, modeled on the forward-deployed engineering pattern, and is ideal for people who want ownership, are comfortable with ambiguity, and measure success by what reaches production at the client rather than what is merely presented. We work in the clients environment, using their vocabulary and timelines, while helping shape AI-native, Enterprise AI, and Applied AI solutions such as assistants, RAG systems, agents, agentic workflows, and autonomous components. We also value learning that improves our broader AI architecture practice, including patterns, reference architectures, and practical insight from real client work.

last updated 40 week of 2026