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Senior Forward Deployed Engineer

Eliza
CompanyEliza
CategoryEngineering
LocationRemote, US
RemoteRemote
EmploymentNot stated
LevelSenior
SalaryNot stated by the employer
Posted28 Jul 2026
Last verified30 Jul 2026
SourceEmployer career page (ashby)
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Description
SENIOR FORWARD DEPLOYED ENGINEER ABOUT US Eliza is a technology services company and Advanced-tier OpenAI partner that’s dedicated to helping organizations build and deploy cutting-edge AI solutions. From generative AI and custom LLM integrations to predictive analytics and intelligent automation, we work across industries to bring real-world AI applications to life. Our projects combine deep technical expertise with hands-on client collaboration to solve high-impact problems. ROLE OVERVIEW The Senior Forward Deployed Engineer (Senior FDE) executes the core technical work on our hardest client problems. This is a senior individual contributor role with real depth: you take ambiguous, high-stakes work from problem definition through shipped outcome, own the architecture and the tradeoffs, and set the technical standard clients experience. Senior FDEs go deep on a major engagement or workstream rather than broad across a portfolio. You are the person we send when the problem is genuinely difficult, the requirements are unclear, and the client needs someone who can hold the technical line while still shipping. You will review critical work before it reaches clients and lift the engineers around you through pairing, review, and example. This role is for engineers who want the hardest technical problems and direct client exposure without trading hands-on work for a management track. KEY RESPONSIBILITIES 1. Lead Complex Technical Delivery - Own end-to-end delivery quality for a major engagement, workstream, or solution area. - Translate ambiguous client needs into practical execution plans, technical milestones, and shipped outcomes. - Maintain a clear view of status, risks, blockers, and dependencies within your scope. - Raise quality concerns early and correct course before client confidence is affected. 2. Own Architecture and Technical Tradeoffs - Lead solution architecture and technical decision-making for your workstream. - Make pragmatic tradeoffs between speed, quality, scope, maintainability, cost, latency, and client value—and explain your reasoning. - Review critical technical work before it is shared with clients or treated as production-ready. - Recognize when a problem needs deeper expertise or a second set of eyes. - Turn hard-won solutions into reusable patterns that raise the floor for future engagements. 3. Build and Deploy Production AI Systems - Design and ship AI/ML solutions using Python, modern ML frameworks, LLM orchestration tooling, and cloud-native infrastructure. - Integrate LLMs and other generative models into client products and workflows at production quality. - Own fine-tuning, prompt engineering, retrieval, and evaluation pipelines where the problem calls for them. - Make sound decisions about security, data privacy, and compliance constraints in enterprise environments. 4. Earn and Maintain Client Trust - Act as the senior technical voice on your engagement, credible with both engineers and executives. - Communicate direction, tradeoffs, risks, and progress clearly to technical and non-technical audiences. - Manage expectations with discipline when scope, timeline, data, or technical constraints change. - Keep account and commercial partners informed so client strategy reflects delivery reality. 5. Handle Risk with Judgment - Spot technical, delivery, scope, timeline, and client-alignment risks early. - Resolve what you can directly; escalate what you cannot, with clear context, options, and a recommendation. - Intervene decisively within your scope when quality, pace, or client confidence is at risk. 6. Lift the Engineers Around You - Mentor less experienced FDEs through technical review, pairing, and practical feedback. - Help teammates make better architecture, implementation, and communication decisions. - Model what excellent forward-deployed execution looks like in client-facing work. - Contribute to interview loops
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