AI Staff Engineer

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    "description": "<p>Location: Norwich Office or Sofia office <br>Reports to: SVP Engineering <br>Type: Permanent, full-time <br>Level: Staff IC <br>Why we're hiring <br>We've got an AI strategy with two pillars: making our own teams faster, and shipping AI into <br>our platform. Adoption is moving. Funding is in place. OpenAI, Cursor, Claude Code, and <br>Bedrock are all live in some form. <br>What we don't have is a single person who owns the platform underneath all of it. That's this <br>role. <br>You'll build and run the shared platform we use for AI: model access, cost governance, <br>evaluation, safety. When a squad says \"we should use AI for that\", they shouldn't have to <br>start from scratch. <br>What you'll own <br>The AI gateway. A paved way for engineers and product squads to use the AI tools we've <br>picked. Consistent auth, logging, fallbacks, and cost attribution. <br>Bedrock and AgentCore. Lead our adoption. We're evaluating AgentCore for agentic <br>workloads now. You'll take it through to production: architecture, cost model, integration with <br>the rest of our AWS estate. <br>Cost governance. Per-tribe visibility. Alerts before the bill, not after. Tied to where the <br>spend is paying off and where it isn't. <br>Evaluation. A standard way to test AI tools and features, and to catch regressions when <br>models change underneath us. <br>Safety. Prompt injection, PII, output filtering, audit trails. Pragmatic, proportionate to the <br>risk, not bureaucratic. <br>Adoption. Building the platform isn't enough on its own. You'll work with EMs and Staff <br>engineers across all five tribes to make sure it gets used, and the patterns we learn get <br>spread. <br>The AI Guild. A cross-tribe group that decides what we adopt, what we retire, and what's <br>worth experimenting with next. You'll run it. <br>Success metrics. Define what good looks like for internal AI tooling (cycle time, defect rate, <br>time saved) and for product AI features (quality, latency, cost per request, customer <br>outcome). <br>What success looks like <br>By six months <br>● AI gateway in production, used by at least one internal tool and one product feature. <br>● Cost dashboard in production. EMs can see what their tribe is spending. <br>● AgentCore and Bedrock evaluation done. A clear go / no-go with production evidence <br>behind it. <br>● First evaluation suite running against real AI features. <br>● AI Guild meeting regularly with people from all five tribes turning up. <br>By twelve months <br>● All product AI features go through the gateway. No squad is rolling its own. <br>● Every team shipping AI uses the standard eval pattern. <br>● AI spend is predictable and tied to value. Not necessarily lower; governed. <br>● Measurable cycle-time gains on at least two engineering workflows we can attribute <br>to internal AI tooling. <br>● RapidAI use cases shipping through the platform. <br>By two years <br>● AI is a normal engineering capability, not a special programme. New features take <br>days to wire up, not weeks. <br>● We can swap models without rewriting product features. <br>● AI cost, latency, and eval data show up in engineering decisions the same way DB <br>performance does today. <br>What we want from you <br>We care about how you think and what you've shipped. That said: <br>● You ship. You write code, dashboards, and runbooks that other engineers use. <br>You're not someone who'll spend three months on a strategy deck. <br>● You think in platforms. You build the version that works for everyone, not a <br>bespoke solution for each squad. <br>● You can hold a room. Staff engineers in the morning, a VP in the afternoon. You can <br>explain the same trade-off to both without losing either. <br>● You've changed your mind about AI before, based on evidence. You can tell us <br>about a use case where AI didn't pay off. <br>● You know the unit economics. You can tell the difference between \"AI is <br>expensive\" and \"this pattern is expensive, here's a cheaper one\". <br>● You understand the benefits and the risks of an AI first approach running at scale. <br>Tradeoffs between public models and self hosted solutions <br>● You know Bedrock in production. We're an AWS shop and Bedrock is our strategic <br>substrate. You should already have the IAM, VPC, throughput, and observability <br>scars. AgentCore experience is a big plus given where we're going. <br>Useful, not required <br>● AgentCore in production, or a comparable agent runtime (LangGraph Platform, <br>Vercel AI SDK, in-house) <br>● Built or operated an LLM gateway <br>● Built or run an eval framework in production <br>● Owned cost governance on a meaningful AI workload <br>● Shipped customer-facing AI and handled the security and legal conversations that <br>come with it <br>● Run a Cursor or Copilot rollout and know what made adoption stick <br>● Background in Platform, DevEx, ML Platform, or Applied AI. We're open. <br>How we work <br>● 5 engineering tribes (Money, POS, Business, Data, Platform), ~120 engineers. <br>● Offices in Norwich and Sofia. <br>● AWS-native. GitLab. Slack-first. <br>● OpenAI, Cursor, Claude Code, are in real use. AWS RapidAI funding is unlocking <br>customer-facing AI work. <br>● UK fintech SaaS scale-up. Sales-led, cashflow-conscious, willing to invest where the <br>upside is real. <br>● You'll report directly to me. Clear remit, exec sponsorship, the air cover to make <br>decisions stick. </p>",
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