[All remote jobs](https://jobicy.com/jobs.md)[![Smartsheet logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/06/8b6103bd-221.jpg)](https://jobicy.com/company/smartsheet.md)[Smartsheet](https://jobicy.com/company/smartsheet.md) # Sr. Forward Deployed AI Engineer (Remote Eligible in the UK) Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/smartsheet.md)ShareRemote from[UK](https://jobicy.com/job-region/uk.md)SalaryUndisclosedDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished13 Oct 2026Apply before12 Nov 2026Listing views30Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Smartsheet is seeking a senior Forward Deployed AI Engineer to lead complex enterprise AI implementations from discovery through production deployment and handoff. The role combines hands-on software engineering with customer-facing solution architecture, including multi-agent orchestration, MCP/tool-use integrations, RAG optimization, and evaluation frameworks. The engineer will create reusable deployment kits, run technical workshops, enable consultants and partners, and mentor junior FDEs. Candidates need deep Python and TypeScript/JavaScript expertise, substantial production AI/ML experience, enterprise stakeholder management skills, and eligibility to work remotely in the UK. Travel of approximately 25–50% domestically and internationally is required. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a highly senior, ambiguous role requiring ownership of complex customer deployments, production-grade AI architecture, and reusable internal platform assets. Success depends equally on advanced engineering judgment, enterprise consulting credibility, and the ability to scale practices through enablement and mentorship. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$190,000US market range$165k–$230k0$253k AI insightNo actual salary, base-pay range, or other role payment was disclosed. These figures are estimated annual US-market base salary benchmarks in USD for a senior forward-deployed AI/software engineer with enterprise deployment leadership and production LLM specialization; actual UK compensation may differ materially. ## Core skills Skills and capabilities most closely associated with this opportunity. [Python](https://jobicy.com/jobs?search_keywords=Python.md)[TypeScript](https://jobicy.com/jobs?search_keywords=TypeScript.md)[JavaScript](https://jobicy.com/jobs?search_keywords=JavaScript.md)[LLM deployment](https://jobicy.com/jobs?search_keywords=LLM%20deployment.md)[Multi-agent orchestration](https://jobicy.com/jobs?search_keywords=Multi-agent%20orchestration.md)[Model Context Protocol (MCP)](https://jobicy.com/jobs?search_keywords=Model%20Context%20Protocol%20MCP.md)[Retrieval-augmented generation (RAG)](https://jobicy.com/jobs?search_keywords=Retrieval-augmented%20generation%20RAG.md)[Enterprise solution architecture](https://jobicy.com/jobs?search_keywords=Enterprise%20solution%20architecture.md)[Cloud infrastructure](https://jobicy.com/jobs?search_keywords=Cloud%20infrastructure.md)[Technical consulting](https://jobicy.com/jobs?search_keywords=Technical%20consulting.md) Sample interview questionsDescribe a complex enterprise AI deployment you led from discovery to production. How did you manage technical and stakeholder risks?I would begin with discovery workshops to define measurable business outcomes, workflow constraints, data boundaries, and security requirements. I would translate those findings into an architecture and phased delivery plan, validate assumptions through a limited pilot, and maintain a decision log with customer stakeholders. Before production, I would establish evaluation criteria, observability, access controls, runbooks, and an operational handoff plan. How would you select an orchestration pattern for a multi-agent customer workflow? I would first assess whether the workflow needs deterministic routing, parallel specialist work, iterative planning, or human approval gates. I generally prefer the simplest pattern that meets reliability requirements, using clear agent contracts, constrained tools, structured outputs, and explicit state management. I would add evaluators, traceability, and fallback paths before increasing autonomy. What is your approach to designing an MCP resource pack for a customer? I would identify the customer systems, user roles, permissions, priority tasks, and data freshness requirements before defining resources and tools. The pack should expose only useful, governed context through well-documented schemas, least-privilege access, and predictable error handling. I would version the integration, instrument usage and failures, and provide examples and runbooks so solutions teams can deploy and support it independently. How do you evaluate and improve a production RAG system? I would create a representative evaluation set from approved customer tasks and measure retrieval quality, groundedness, task completion, latency, and cost. I would investigate failures by separating retrieval, context construction, model reasoning, and tool-execution issues. Improvements might include better chunking and metadata, hybrid retrieval, reranking, query rewriting, source citations, or workflow changes, followed by regression testing. How would you enable non-engineering solutions consultants to use a deployment kit safely? I would make the kit opinionated and operationally complete: prerequisites, configuration templates, step-by-step deployment instructions, validation checks, troubleshooting guidance, and clear escalation criteria. Training would use realistic scenarios and certification exercises to demonstrate both implementation and safe customer communication. I would collect field feedback, track common failure points, and continuously simplify the kit based on that evidence. Opportunity details ## About this role. For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day. You independently lead the most complex AI deployments Smartsheet undertakes. You own the full engagement lifecycle from technical discovery through production deployment through solutions org handoff. You architect multi-agent solutions, design client-personalized MCP resource packs, and build the Deployment Kits that transform how 200+ solutions consultants and partners operate. You mentor junior FDEs, drive the intelligence loop, and present field findings at weekly Applied AI strategy sessions. You are building a function, not filling a role. What You Will Do * Lead complex, multi-system AI deployments end-to-end scope, architect, build, validate, and manage the customer relationship throughout. * Own the AI workshop program for your pod, customize modules per customer, lead technical sessions, translate outputs into production requirements, evolve content from field learning. * Architect multi-agent solutions selecting the right coordination pattern for each customer’s workflow characteristics and compliance requirements. * Design client-specific and industry-specific MCP resource packs that serve personalized intelligence from the server so every connected AI surface gets smarter for that customer automatically. * Own Deployment Kit quality for your pod. If a kit is not documented well enough for a solutions consultant with no engineering background to follow, it isn’t done. * Lead Solutions Enablement Sprints: transfer AI deployment patterns to solutions consultants and partners with training materials and certification criteria. * Drive the intelligence loop: author strategic memos, present field findings weekly, contribute Agent Bricks and Skills, file RFCs for platform improvements. * Mentor junior FDEs on customer engagements, code reviews, and Deployment Kit quality. Required * 6–10+ years production software engineering, including 3+ years deploying AI/ML to production. Deep Python; strong TypeScript/JavaScript. * Proven track record leading complex enterprise technical engagements. You’ve managed customer relationships, navigated security reviews, and delivered production systems organisations depend on. * Deep production LLM experience: multi-agent orchestration, MCP/tool-use, RAG optimisation, evaluation frameworks, agent deployment at scale. * System architecture expertise spanning enterprise systems, cloud infrastructure, and AI services explainable to a CIO and a junior engineer. * Demonstrated ability to create reusable technical assets that others use in production. You think in platforms, not projects. * Strong written and verbal communication. Architecture documents, runbooks, strategic memos, training materials. Your Deployment Kits are your portfolio. * You leave teams better than you found them. You make sure the people you work with understand what was built, why it works, and how to own it without you. * Ability to travel. This role requires domestic and international travel approximately 25–50% of the time. * Elligible to work in the UK on an ongoing basis * BS or MS in Computer Science, a related field, or equivalent industry experience Nice to Have: * Experience founding or scaling an FDE/solutions engineering function. * Technical founder background. Databricks, AWS Bedrock, or Temporal experience. * Deep domain knowledge in healthcare, financial services, or government. Agent framework contributions (Strands SDK, LangChain, open-source MCP servers). * Proficiency in German/French language will be an added advantage * Familiarity of AI governance, ethics, and regulatory requirements (e.g. GDPR, UK/EU AI Act) Get to Know Us: At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. You’ll have the freedom to explore, push boundaries, and grow beyond your role. We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Let’s build what’s next, together. Equal Opportunity Employer: Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, India, and Singapore. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information. If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know. #LI-Remote Show more [Apply now >](https://jobicy.com/jobs/155167-sr-forward-deployed-ai-engineer-remote-eligible-in-the-uk.md) > Annual salary information is not provided for this position. 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