[All remote jobs](https://jobicy.com/jobs.md)[![WorkWave logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2022/03/78458cfe50d13ec79a5afc7a7a557272.jpeg)](https://jobicy.com/company/workwave.md)[WorkWave](https://jobicy.com/company/workwave.md) # Senior AI Engineer, Agentic Platform 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/workwave.md)ShareRemote from[UK](https://jobicy.com/job-region/uk.md)SalaryUndisclosedDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceSeniorPublished1 Nov 2026Apply before1 Dec 2026Listing views47Application 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. WorkWave seeks a senior, product-minded AI engineer to own production agent capabilities for its in-product assistant, AskWaive. The role covers building Amazon Bedrock-based agents, MCP tools, retrieval workflows, runtime guardrails, and the serving path. The engineer will create evaluation harnesses, monitor groundedness and hallucinations, and iterate from user adoption and quality signals. This is a high-ownership position requiring strong Python, AWS, SQL, LLM-agent engineering, and cross-functional product communication. Candidates must be UK-based and already have the legal right to work in the UK. ## 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 senior end-to-end engineering role that combines production LLM systems, data grounding, evaluation, infrastructure concerns, and product outcomes. The engineer is expected to independently diagnose failures, influence platform changes, and operate reliable agent experiences at scale. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$165,000US market range (per year)$140k–$200k0$220.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The displayed figures are estimated annual USD market compensation for a senior AI/LLM platform engineer and are not employer-provided pay. ## Core skills Skills and capabilities most closely associated with this opportunity. [Python](https://jobicy.com/jobs?search_keywords=Python.md)[Amazon Bedrock](https://jobicy.com/jobs?search_keywords=Amazon%20Bedrock.md)[LLM agent engineering](https://jobicy.com/jobs?search_keywords=LLM%20agent%20engineering.md)[Model Context Protocol](https://jobicy.com/jobs?search_keywords=Model%20Context%20Protocol.md)[Retrieval-augmented generation](https://jobicy.com/jobs?search_keywords=Retrieval-augmented%20generation.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[Agent orchestration](https://jobicy.com/jobs?search_keywords=Agent%20orchestration.md)[LLM evaluation](https://jobicy.com/jobs?search_keywords=LLM%20evaluation.md)[Prompt and context engineering](https://jobicy.com/jobs?search_keywords=Prompt%20and%20context%20engineering.md)[Observability](https://jobicy.com/jobs?search_keywords=Observability.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[API design](https://jobicy.com/jobs?search_keywords=API%20design.md)[Production monitoring](https://jobicy.com/jobs?search_keywords=Production%20monitoring.md)[Product analytics](https://jobicy.com/jobs?search_keywords=Product%20analytics.md) Sample interview questionsDescribe an LLM agent you took from prototype to production. What changed after the initial demo?A strong answer should explain the production architecture, tool boundaries, retrieval or context strategy, error handling, observability, evaluation datasets, and measurable user or business outcomes. It should also identify concrete reliability, latency, cost, or grounding problems discovered after launch and how they were addressed. How would you evaluate whether an agent is grounded in company data and avoids hallucinations? I would define representative labelled scenarios, expected evidence, and quality rubrics for correctness, citation support, appropriate refusal, and task completion. I would run these in automated regression tests, track failure categories over time, and combine offline scores with production feedback, traces, and sampled human review. How would you design an MCP tool for an agent that needs to retrieve operational metrics? I would provide typed, narrowly scoped inputs and outputs, explicit authorization and validation rules, predictable error responses, and metadata that clarifies freshness and metric definitions. I would avoid exposing overly broad raw data access, instrument tool calls, and test edge cases such as ambiguous entities, missing data, and conflicting filters. What signals would you use to decide whether an agent feature is successful for customers? I would combine adoption and repeat usage with task completion, time saved, decision quality, escalation or correction rate, and qualitative feedback. Success metrics should be defined with Product before launch, segmented by user workflow, and interpreted alongside groundedness, latency, and cost metrics to avoid optimizing engagement alone. How do you safely use coding agents in a production engineering workflow? I use coding agents to accelerate implementation, but rely on clear specifications, typed interfaces, targeted tests, code review, and CI gates to verify output. I treat generated code as untrusted until it passes unit, integration, security, and regression checks, and I improve reusable scaffolding so future tasks are safer and faster. Opportunity details ## About this role. We are looking for a product-minded AI engineer to turn what our customers are trying to decide into agents they actually rely on. AskWaive, our in-product assistant, is already live; the work now is quality, coverage, and an experience that wows. This is not a role for a prompt author or a demo builder. We want an owner: someone who takes a customer need—already expressed through our serving layers and semantic layer—and turns it into an endpoint, a tool, and an experience that makes the customer measurably better at their job. Agent engineering, deployment, and testing are all part of delivery, not a handoff. Our core data platform is owned by the team that built it. You’ll build on top of it—and where data or infrastructure is holding an agent back, you’ll have the standing to propose the change and drive it. ### What You’ll Do From Customer Need to Product Superpower * Own the Outcome: Start from the customer, and stay with it until the agent is answering their needs better * Build Agents and Tools: Build production agents and the tools they call—retrieval, multi-step workflows, action-taking—on Amazon Bedrock, with Bedrock AgentCore as our runtime and tools exposed over MCP (Model Context Protocol). * Turn Serving Layers Into Capability: Our semantic layer and service APIs are where customer needs are already expressed. Your job is turning them into endpoints and tools an agent can wield well—and knowing which metrics and entities it should reach at all. * Shape the Experience, Not Just the Endpoint: How an answer arrives—its speed, its confidence, its explanation, the next step it offers—is the product. A technically correct response a customer ignores is a failure. Prove It and Make It Felt * Measure Groundedness: Build the eval harness—labeled test sets, groundedness and hallucination rates you can put a number on, and CI gates that stop a regression before it ships. * Measure What Customers Got: Adoption, feedback, intent—then report realized impact back to Product and the business in numbers that hold up. * Diagnose and Iterate: Instrument the tracing that shows why an answer was wrong, turn each failure mode into a specific fix, and ship again. This loop is the job, not a phase of it. Deliver * Ship and Operate: Own the serving path—MCP endpoints, latency, cost per response, caching, and runtime guardrails. Deployment and testing are part of the job, not a handoff. * Embed and Influence: Partner with Product Managers and engineers on which decisions an agent should support—and which it shouldn’t. Where data or infrastructure limits what an agent can do, drive the change alongside the team that owns the platform. ### Who You Are The Owner: You measure your work by whether customers made better decisions with the agent’s help, not by whether it shipped. The Agent Builder: You’ve taken an agent past the demo. You know the hard part is everything after it works once—coverage, grounding, cost, and the failure modes you only see at scale. You fix problems without waiting for a ticket. The Experience Obsessive: You care how an answer lands, not just whether it was right. Trustworthy, fast, and legible beats clever. We know that great talent comes from many backgrounds. If you are a builder who cares about the “why” behind the code, we want to hear from you! HOW WE WORK We build with coding agents. You set direction and targets, review output critically, and build the harnesses—scaffolding, context, tests, review loops—that make the next task faster for the whole team. The leverage is in the verification: the tests, CI gates, and typed interfaces that make agent-written code safe to merge. ### What You”ll Bring * Experience: 5+ years building production software or data-intensive systems used by real customers, with strong Python skills—including at least one LLM or agent feature you shipped to production users. * AWS & Agent Stack: Hands-on AWS experience, ideally Amazon Bedrock and Bedrock AgentCore. Equivalent agent runtimes transfer, but we build on AWS and you’ll be productive faster if you have prior exposure. * Agent Engineering: Orchestration, tool and function design, context assembly, and multi-step workflows—you’ve made the design calls, not just the prompt calls. * Retrieval & Domain Grounding: You’ve built retrieval over real business data, you can explain how context gets selected and why an answer was wrong, and you have enough SQL and metric fluency to catch an agent answering with the wrong number. * Evaluation & Iteration: You’ve built or owned an eval loop for a generative system—labelled sets, regression gates, and a number you could defend when someone asked whether it got better. * Product Sense for AI: You have opinions about how an agent should behave—when to answer, when to ask, when to refuse, how to show its work—and you’ve shipped those opinions, not just argued them. * Measurement & Narrative: You’ve quantified what an agent actually delivered for users—adoption, task success, decision quality—and presented it to people who were never going to read your traces. * Working With Agents: You’ve used coding agents on real production work, you can tell correct output from merely plausible output, and you invest in the harnesses that make the next task faster. * Communication & Collaboration: Excellent communication and collaboration skills, with a track record of working across technical and non-technical teams. Bonus Points For: * LLM observability tooling (Langfuse, LangSmith, or Arize). * Semantic layer or metrics-layer design (Ossie, dbt). * Field-service, logistics, or other operational SaaS domains. * Prior experience as a senior or lead engineer responsible for guiding technical direction. Location: Remote (UK-based) with occasional collaborative team meetups. Right to Work: Applicants must currently have the legal right to work in the UK. We are unable to provide visa sponsorship for this position at this time. ### Compensation ### Additional Information WHAT YOU SHOULD KNOW ABOUT US * Laid back but focused: We offer a flexible, collaborative work environment and a passion for developing innovative, best-in-class solutions that directly contribute to the success of our customers. * Impact-driven: We care deeply and deliver services and solutions that make a real difference in the lives of our clients and their businesses. * Trust & Inclusion: We openly accept others as they are and build strong partnerships based on trust and mutual respect. RELAX, WE’VE GOT YOU COVERED * Competitive company pension scheme with employer match. * Generous holiday allowance / flexible time off plus UK bank holidays. * Enhanced maternity, paternity, and parental leave policies. * Ongoing learning resources. * Comprehensive Employee Assistance Programme (EAP) providing free 24/7 mental health, legal, and financial guidance. EQUAL OPPORTUNITY WorkWave is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees in accordance with the Equality Act 2010. Don’t meet every single requirement? Studies show that women and underrepresented groups are less likely to apply for jobs unless they meet every qualification. If you feel you could make a great impact in this role, we encourage you to apply anyway! Show more [Apply now >](https://jobicy.com/jobs/158318-senior-ai-engineer-agentic-platform.md) > Annual salary information is not provided for this position. 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