H3About The Role /h3pWe are looking for a hands-on Forward-Deployed AI Architect to lead the technical delivery of an AI-assisted operations platform for an enterprise client in travel disruption management. /ph3About The Role /h3pWe are looking for a hands-on Forward-Deployed AI Architect to lead the technical delivery of an AI-assisted operations platform for an enterprise client in travel disruption management. /ppThe current phase covers three connected workflows: /pulliProcessing and routing service requests /liliRanking and recommending hotels /liliClosing cases with billing reconciliation and downstream system handoff /li /ulpYou will turn operational knowledge and existing software into a working, testable system with explicit human approvals and traceable decisions. /ppThis is technical leadership, not architecture-only advice or task execution. You will work directly with operational experts, the client’s technology partners and our engineers, challenge assumptions constructively, and carry the technical solution from discovery through implementation and evaluation. /ph3How you will work /h3pYou own the technical approach, engineering plan, implementation quality and evidence of readiness. You partner with the engagement Principal, who owns the integrated delivery plan, commercial scope and client commitments. You provide feasibility assessments before commitments are made and elevate unresolved dependencies or scope changes with options and their impact. /ph3What you will own /h3ulliEstablish an evidence-based technical baseline /liliInspect the code, infrastructure, APIs and operator workflows before estimating the build. /liliTranslate discovery into concise specifications with inputs, outputs, exclusions, acceptance tests and source references. /liliDistinguish confirmed requirements from analyst recommendations and assumptions. /liliResolve contradictions with the appropriate owner. Do not implement them silently. /liliMake integration work early /liliBuild and review critical components,



guide engineers and prove a thin end-to-end path in the client’s test environment: receive a request, retrieve operational data, produce a recommendation, obtain approval and perform the agreed system handoff. /liliVerify API access, write capabilities, event contracts and error paths. Do not treat documentation or isolated components as proof of readiness. /liliMake rules and knowledge maintainable /liliDesign a clear separation between operational facts, business policies and software logic. /liliWork with named business owners to define rule precedence, exceptions, validation and approval. /liliKeep draft or inferred rules separate from approved rules. /liliSupport versioned updates without a software release, with historical replay, traceable approval and rollback. /liliUse grounded reasoning and human escalation where rules do not resolve a case. /liliOwn engineering quality and evaluation /liliReview and test code, including AI-generated code, against the agreed architecture. /liliEstablish automated CI checks, integration tests and representative operational cases. /liliTrack the exact application, rule and data versions evaluated. /liliImplement decision logs, monitoring, access controls and failure recovery. /liliDefine test-readiness evidence and agree workflow-quality, latency and operating-cost measures with the team and client. /liliKeep technical decisions and progress clear /liliMaintain a concise architecture and decision record that shows what works, what remains and why. /liliRaise blockers early with evidence, an owner, a decision needed and delivery impact. /liliPropose simpler alternatives and update estimates when assumptions change.



/liliDemonstrate working increments, and clearly distinguish prototypes from integrated functionality. /liliLeave the team with maintainable code, documentation and a practical handover. /li /ulh3What good looks like /h3ulliBefore build commitments: a reviewed technical baseline, verified critical dependencies and explicit assumptions behind the estimate. /liliDuring delivery: a working end-to-end increment, automated tests running in CI, and demonstrated progress against agreed acceptance cases. /liliFor rule changes: a named business approver, a versioned baseline and recorded replay results before promotion. /liliAt evaluation: reproducible results, decision traces and documented limitations that support the agreed Go/No-Go decision, not just a successful demo. /li /ulh3What you bring /h3ulliEvidence of taking an applied AI or LLM-enabled workflow beyond a prototype into operational use. /liliStrong hands-on software engineering, architecture, API integration and code-review skills. /liliExperience turning incomplete or conflicting discovery material into implementable, testable specifications. /liliSound judgment about structured rules, retrieval, AI reasoning and human review, including their failure modes. /liliPractical experience with evaluation datasets, regression testing, CI and production observability. /liliConfidence to challenge assumptions, explain alternatives and elevate decisions constructively across business and engineering teams. /liliClear, concise communication and realistic estimation based on verified dependencies and available capacity. /li /ulh3Nice to have /h3ulliExperience in exception-heavy operations, business-rule systems, travel, hospitality or logistics /liliFamiliarity with AWS-based AI deployments /li /ulh3Logistics /h3pThe role includes an in-person working session in Milan. Please share your earliest start date, realistic weekly availability and ability to travel. /p #J-18808-Ljbffr

📌 Forward-deployed ai architect (Roma)
🏢 Neurons Lab
📍 Roma