Confirmed on the employer's own hiring board on Oct 19, 2026. First seen by Alion on Jul 14, 2026. Calliere scores B on the Alion truth index.
Responsibilities Lead technical discovery directly with a business stakeholder; turning vague, ambiguous pain points into a scoped, buildable engineering problem Architect and build production-grade AI systems: model/provider selection, retrieval and grounding strategy, guardrails, graceful degradation and fallback paths Make build-vs-buy, custom-vs-platform, and no-code-vs-custom-code calls for each engagement Own reliability end-to-end; error handling, retries, idempotency, monitoring, permissioning, rollback strategy Review and unblock architectural decisions made by less senior engineers on your pod Present and defend technical tradeoffs directly to client leadership Turn what you build into reusable patterns, templates, and evaluation frameworks for the broader platform team Partner with senior leadership on scoping and staffing upcoming engagements Requirements 5+ years shipping software or AI-enabled systems into production, with direct ownership of architecture decisions Experience building for external clients or business stakeholders, not just internal engineering teams Strong communicator; comfortable running discovery conversations with non-technical leaders and translating fluidly between business and engineering language Deep, hands-on fluency with AI-enabled workflows: prompting, retrieval/grounding, model and provider selection, guardrail and fallback design Solid software engineering fundamentals; APIs, integrations, data contracts, auth/permissions, production-scale reliability Comfort owning ambiguity: scoping the problem yourself, making the tradeoffs, and standing behind them A track record of tools that were actually adopted and used; not prototypes that quietly died Nice to have: Prior forward-deployed engineering experience at a well-known technical consultancy, or embedded/on-site technical roles inside client organizations Exposure to private equity operating environments, portfolio companies, or similarly fast-moving, high-stakes settings Experience mentoring or technically leading other engineers Familiarity with enterprise systems (CRM, ERP, ITSM, HRIS) and integrating AI tooling into them A background translating SME or business-leader domain knowledge directly into product requirements
Benefits How Success Looks Here You build trust with business leaders as fast as you build the system itself. You turn messy, ambiguous business pain into something scoped and shippable without needing hand-holding. You default to shipping working tools over decks and diagrams. Reliability isn't optional to you. You're direct, low-ego, and know when to push back on a client's assumptions; and you mentor by unblocking people, not gatekeeping.

