AI Staff Engineer

Reports to: SVP Engineering

Type: Permanent, full-time

Level: Staff IC

• Why we're hiring
• We've got an AI strategy with two pillars: making our own teams faster, and shipping AI into
• our platform. Adoption is moving. Funding is in place. OpenAI, Cursor, Claude Code, and
• Bedrock are all live in some form.
• What we don't have is a single person who owns the platform underneath all of it. That's this
• role.
• You'll build and run the shared platform we use for AI: model access, cost governance,
• evaluation, safety. When a squad says "we should use AI for that", they shouldn't have to
• start from scratch.
• What you'll own
• The AI gateway. A paved way for engineers and product squads to use the AI tools we've
• picked. Consistent auth, logging, fallbacks, and cost attribution.
• Bedrock and AgentCore. Lead our adoption. We're evaluating AgentCore for agentic
• workloads now. You'll take it through to production: architecture, cost model, integration with
• the rest of our AWS estate.
• Cost governance. Per-tribe visibility. Alerts before the bill, not after. Tied to where the
• spend is paying off and where it isn't.
• Evaluation. A standard way to test AI tools and features, and to catch regressions when
• models change underneath us.
• Safety. Prompt injection, PII, output filtering, audit trails. Pragmatic, proportionate to the
• risk, not bureaucratic.
• Adoption. Building the platform isn't enough on its own. You'll work with EMs and Staff
• engineers across all five tribes to make sure it gets used, and the patterns we learn get
• spread.
• The AI Guild. A cross-tribe group that decides what we adopt, what we retire, and what's
• worth experimenting with next. You'll run it.
• Success metrics. Define what good looks like for internal AI tooling (cycle time, defect rate,
• time saved) and for product AI features (quality, latency, cost per request, customer
• outcome).
• What success looks like
• By six months
• ● AI gateway in production, used by at least one internal tool and one product feature.
• ● Cost dashboard in production. EMs can see what their tribe is spending.
• ● AgentCore and Bedrock evaluation done. A clear go / no-go with production evidence
• behind it.
• ● First evaluation suite running against real AI features.
• ● AI Guild meeting regularly with people from all five tribes turning up.
• By twelve months
• ● All product AI features go through the gateway. No squad is rolling its own.
• ● Every team shipping AI uses the standard eval pattern.
• ● AI spend is predictable and tied to value. Not necessarily lower; governed.
• ● Measurable cycle-time gains on at least two engineering workflows we can attribute
• to internal AI tooling.
• ● RapidAI use cases shipping through the platform.
• By two years
• ● AI is a normal engineering capability, not a special programme. New features take
• days to wire up, not weeks.
• ● We can swap models without rewriting product features.
• ● AI cost, latency, and eval data show up in engineering decisions the same way DB
• performance does today.
• What we want from you

We care about how you think and what you've shipped. That said

• ● You ship. You write code, dashboards, and runbooks that other engineers use.
• You're not someone who'll spend three months on a strategy deck.
• ● You think in platforms. You build the version that works for everyone, not a
• bespoke solution for each squad.
• ● You can hold a room. Staff engineers in the morning, a VP in the afternoon. You can
• explain the same trade-off to both without losing either.
• ● You've changed your mind about AI before, based on evidence. You can tell us
• about a use case where AI didn't pay off.
• ● You know the unit economics. You can tell the difference between "AI is
• expensive" and "this pattern is expensive, here's a cheaper one".
• ● You understand the benefits and the risks of an AI first approach running at scale.
• Tradeoffs between public models and self hosted solutions
• ● You know Bedrock in production. We're an AWS shop and Bedrock is our strategic
• substrate. You should already have the IAM, VPC, throughput, and observability
• scars. AgentCore experience is a big plus given where we're going.
• Useful, not required
• ● AgentCore in production, or a comparable agent runtime (LangGraph Platform,
• Vercel AI SDK, in-house)
• ● Built or operated an LLM gateway
• ● Built or run an eval framework in production
• ● Owned cost governance on a meaningful AI workload
• ● Shipped customer-facing AI and handled the security and legal conversations that
• come with it
• ● Run a Cursor or Copilot rollout and know what made adoption stick
• ● Background in Platform, DevEx, ML Platform, or Applied AI. We're open.
• How we work
• ● 5 engineering tribes (Money, POS, Business, Data, Platform), ~120 engineers.
• ● Offices in Norwich and Sofia.
• ● AWS-native. GitLab. Slack-first.
• ● OpenAI, Cursor, Claude Code, are in real use. AWS RapidAI funding is unlocking
• customer-facing AI work.
• ● UK fintech SaaS scale-up. Sales-led, cashflow-conscious, willing to invest where the
• upside is real.
• ● You'll report directly to me. Clear remit, exec sponsorship, the air cover to make
• decisions stick.