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    Lead Forward Deployed Engineer, Hospitality

    Any, United StatesRemoteFull-timePosted 1mo ago
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    Job description

    telostravel.ai
    Lead Forward Deployed Engineer, Hospitality
    Remote (US or Canada) · Hybrid/Remote · Reports to the Chief Operating Officer
    About telos
    telos is an AI revenue decision platform: a decision-infrastructure layer that sits between systems of record (PMS, CRS, RMS, distribution) and the people making high-yield commercial decisions on pricing, inventory, mix, and merchandising. We amplify the judgment of revenue teams by surfacing more good decisions than any team has time to find on its own. Our airline business is live in production with major carriers, and we are now building the equivalent capability for hospitality. This role delivers it, hotel by hotel and brand by brand.
    The Role
    Forward deployed engineers at telos ship working AI inside client environments. As Lead Forward Deployed Engineer, you sit at the intersection of hotel revenue management practice and applied AI engineering. You embed with hotel commercial teams, own end-to-end delivery on their real data and real systems and stay until revenue managers trust the output enough to act on it.
    The hard part of AI in hospitality is not the model. It is the workflow around it: whether revenue managers trust the system, whether it fits how commercial decisions get made, and whether it holds up under the operational pressure of running a hotel portfolio. You have lived that pressure, from the engineering side, the commercial side, or ideally both, and you know how to build for it.
    You start as a senior individual contributor with full ownership of client deployments and grow into building the forward deployed engineering team as the hospitality business scales.
    What You'll Do
    • Own client deployments end to end: scope the engagement, integrate with the client’s PMS, CRS, RMS, and distribution stack, ship the models, and drive adoption inside the revenue organization.
    • Design and ship agentic workflows that revenue managers actually adopt rather than second-guess.
    • Translate deep hotel revenue management practice into decision logic, validating that agent recommendations match the judgment of seasoned revenue managers and directors.
    • Work on-site and remotely with client product, engineering, and commercial leadership to integrate cleanly with hotel systems of record and distribution.
    • Define the methodology and metrics standards each deployment is held to, ensuring rigor in how performance is framed and measured.
    • Represent telos credibly with brand-level revenue and commercial leaders, the EVPs and heads of revenue who buy and rely on this work.
    • Build, mentor, and lead the forward deployed engineering team as the function scales.
    Who You Are
    You combine hands-on technical delivery with real commercial domain depth. Strong candidates come from one of two paths, and the best come from both:
    • Engineering side: a senior technical leader with production experience building and deploying machine learning, forecasting, optimization, and agentic AI, ideally in client-facing or embedded delivery roles.
    • Commercial side: experience as a revenue analyst, revenue manager, or revenue director at a large hotel brand, owning real pricing and inventory decisions across multi-property portfolios.
    Required: Hotel Systems Fluency
    Demonstrated, hands-on familiarity with the systems hotel revenue teams actually run on, such as:
    • Revenue management systems (RMS): IDeaS, Duetto, Revenue Analytics, or equivalent, deployed in production at scale.
    • Property management systems (PMS): Oracle OPERA or equivalent.
    • Central reservation systems (CRS): Amadeus ACRS, Sabre SynXis, or equivalent.
    • BI and rate-shopping tools: Lighthouse, STR, Kalibri, Amadeus BI, or equivalent.
    • Full command of the hotel revenue management metric set and the methodological distinctions that matter
    Required: Technical and Delivery
    • Deep lived experience across hotel system engineering or hotel revenue management roles (not back-office IT), pairing senior leadership with hands-on execution.
    • Practical experience with major hotel industry APIs such as Oracle Hospitality Integration Platform (OHIP), OpenTravel Alliance (OTA) specs, and Hospitality Technology Next Generation (HTNG) specs.
    • Production experience with SQL, Python and modern ML and optimization tooling; comfort across cloud AI services (AWS, Azure, GCP).
    • Track record of shipping AI that fits real commercial workflows and earns analyst trust under operational pressure.
    • Strong communicator able to move fluently between revenue teams, engineers, and brand-level executives, with a practitioner’s credibility in the room.
    Desired
    • Experience with agentic AI, RAG, Model Context Protocol (MCP), and Universal Commerce Protocol (UCP) architectures.
    • Depth in distribution and channel strategy: OTA and wholesale contracting, channel mix and drift, direct-booking optimization.
    • Experience with customer insights, loyalty, and segmentation analytics.
    • A revenue management certification (e.g., CRME) and continuing industry leadership presence.
    Equal Opportunity
    telos is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by applicable law. telos complies with all applicable pay-transparency requirements; where required by law, the relevant pay range will be provided during the application process.

    Job details are sourced from the employer's original posting.

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    About the company

    UP.Labs

    UP.Labs is a company that focuses on building and scaling innovative products and services. They specialize in taking early-stage concepts and transforming them into fully realized solutions, often starting with a business problem or operational pain point.

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