Forward-Deployed Engineer / Technical Solutions Lead

The company is an early-stage AI infrastructure startup building runtime admissibility controls for externally effective AI and software actions. The initial product focus is AI-generated outputs: before an output is released, saved, exported, sent, or otherwise made externally effective, the system verifies release conditions and produces verifier-readable evidence of the decision.

The team has a working demo, some early IP protections, and active design-partner research underway. We are looking for a fractional technical lead who can help bridge product, engineering, and customer discovery during the organization’s first design-partner pilots.

This is a hands-on, customer-facing technical role. The right person is likely a forward-deployed engineer, solutions architect, field CTO, founding engineer, or senior backend/platform engineer who has worked directly with customers and can move comfortably between architecture diagrams, Python code, APIs, cloud deployment, and founder-level product strategy.

Initial engagement is expected to be around 5–10 hours per week for several months, with flexible hourly compensation and the possibility of expanding the role if pilot traction justifies it.

Responsibilities

• Engage directly with design partners and prospective customers to understand their AI-output workflows, backend architecture, approval paths, and release/finalization events.
• Help identify the specific “commit edge” where an AI-generated output becomes externally visible, saved, exported, sent, or otherwise effective.
• Translate the current demo into partner-specific pilot architectures, including proposed SDK, wrapper, middleware, gateway, webhook, or sidecar integration patterns.
• Prototype and extend Python-based integration code for early partner pilots.
• Assist with API, webhook, authentication, logging, evidence-generation, and verifier-flow implementation for narrow proof-of-concept integrations.
• Help prepare and deliver technical demos for prospective design partners, including sample evidence bundles, verifier-readable receipts, and pilot walkthroughs.
• Work with the founder to turn customer technical feedback into product requirements, backlog priorities, and integration-pattern decisions.
• Draft concise customer-facing technical materials, including integration notes, pilot scopes, runbooks, and architecture diagrams.
• Advise on when the company should hire full-time engineering capacity, what role should come first, and what technical work should remain fractional or outsourced.

Qualifications

• Strong Python backend engineering experience, preferably in infrastructure, developer tooling, security, AI systems, data systems, or enterprise SaaS.
• Experience designing and implementing API integrations using REST, JSON, OAuth, webhooks, event-driven workflows, or similar patterns.
• Comfortable working with cloud deployment primitives such as containers, CI/CD, environment configuration, logging, secrets, authentication, and basic observability.
• Practical familiarity with AI application architecture, including model APIs, inference workflows, prompt/output handling, RAG systems, agentic workflows, or model-serving infrastructure.
• Ability to reason about where outputs become final, customer-visible, stored, exported, sent, or otherwise operationally consequential.
• Strong customer-facing communication skills. Must be able to run technical discovery with partner engineering teams without overclaiming product maturity.
• Experience in early-stage startups, design-partner programs, technical sales engineering, solutions architecture, forward-deployed engineering, or founding engineering roles.
• Available for roughly 5–10 hours per week for several months, with ability to start in the coming weeks.

Preferred experience

• Experience with regulated, high-trust, or audit-sensitive environments such as security, privacy, compliance, fintech, healthcare, legal tech, enterprise AI, or government-facing software.
• Experience helping early technical products move from demo to pilot to repeatable integration pattern.
• Familiarity with AI governance, AI transparency, model-risk management, policy enforcement, provenance, audit logging, or compliance evidence systems.
• Comfort with ambiguous early-stage technical strategy, including deciding what not to build yet.

The immediate need is technical field execution: understanding partner systems, identifying bounded integration points, and helping govern one real AI-output release path at a time.