[Hiring] Applied LLM Engineer @Yonder Media Mobile

Role Description

House of YO is a connectivity and distribution business with compute layered on top. YOlanda is our AI concierge. On V3 of the YO platform, YOlanda is the interface. Users talk to her instead of tapping through menus. She answers questions about plans, data, top-ups and YOYO$ balances, and she takes the action on the user’s account. When she is wrong, the user is stuck and the product has failed.

Her knowledge is the YO ecosystem and nothing else. When a question falls outside it she hands off to YOnC, our compute cooperative, which routes it to a specialist model. Knowing where that line sits, and holding it, is central to the job.

She works to one golden rule: within five responses she has put a product or service in front of the user. Not five responses to be helpful. Five to arrive somewhere real.

This role makes her right, and keeps her fast, affordable and accountable in production. You write the prompts that govern how she thinks, build the evaluations that prove she works, run the caching, routing and controls that let her do it at scale at a cost per turn we can defend, and ship the code behind all of it.

YOlanda runs on our own LangChain and LangGraph build. You will open pull requests against that graph alongside the two engineers who own it. You will not manage anyone. You will be in the model and in production every day.

Qualifications

• Two or more years building with large language models in production, at real traffic. Daily use of a chat window does not count.
• Python you can ship. Scripts, API clients, data handling, tests. Git, JSON, regex, markdown.
• LangChain and LangGraph. You have shipped with both, and you can read a graph you did not write and change it without breaking it.
• Prompting and evaluation. System prompts you wrote that shipped, and the evaluation harness that proved them. Code or work we can read. You change one variable at a time and record the result.
• Production operations. Tracing, SLOs, fallbacks, and caching you have shipped with invalidation that was right. A before and after on cost and latency, with numbers on it.
• Governance in practice. Versioning, release gates, audit logging, personal data handling. Controls in code, not policy in a document.
• Retrieval in production. Chunking, embeddings, reranking, grounding an answer in a source, and knowing when retrieval is the wrong tool for the question.
• Exceptional written English. Syntax, register, nuance. You can write in a voice that is not your own and hold it.
• Care for the user at the other end. Many of our users are on prepaid plans and count every dollar. YOlanda gets it wrong at their expense, not ours.

Requirements

• Own YOlanda’s system prompt. Persona, tone, safety rules, what she refuses, when she hands off.
• Write and maintain the prompts behind every user action on V3: plan changes, top-ups, balance queries, offers, troubleshooting, account support.
• Hold the golden rule. Design the conversation so she reaches a product or service within five responses, without becoming pushy.
• Own the boundary. Decide what is inside YOlanda’s frame of reference and what goes to YOnC, and make the handoff seamless.
• Manage context. Decide what YOlanda knows about the user, the account and the plan catalogue at each step.
• Build the evaluation set for YOlanda. Every task she performs gets test cases, a pass condition and a score you can track over time.
• Measure the boundary, fact check plan prices, data allowances and YOYO$ maths.
• Trace every call. Prompt version, model, tokens, cache hits, latency and cost on every turn.
• Own the gateway behaviour YOlanda depends on: timeouts, retries, and fallbacks across providers and models.
• Write prompts for the cache. Stable parts first, in a fixed order.
• Build semantic, retrieval and tool-result caching, scoped and invalidated correctly.
• Own cost and latency per turn. Token budgets, context trimming, output length, and batch APIs for offline work.
• Route by task. Benchmark frontier and open models on accuracy, latency and cost per turn.
• Keep prompts, models and configuration in git with the rest of the code.
• Build the release gate in CI: evaluation score, cost and latency budget.
• Audit every action that touches an account or money, from the user’s message to the tool call.
• Put the guardrails in the runtime: prompt injection defences, tool permissions, spend limits.
• Set cost budgets per feature and per model, with alerts.

Benefits

• Top-notch products disrupting the world of the traditional media-services industry
• A brilliant, highly collaborative team with a shared drive to achieve goals
• Flexible working hours
• Competitive salary
• Real opportunity for personal and professional growth