Fullstack AI Engineer (Senior) - IFS Logistics

About IFS Logistics

IFS Logistics (built and launched as 7bridges, now part of IFS) is an AI-native enterprise logistics platform. We unify transport data, optimise carrier spend, and automate execution for enterprise shippers and logistics companies. We're a small, dedicated team inside IFS, reporting into the commercial organisation rather than central R&D. Pure SaaS, zero consultancy.

The role

This is not a traditional "add AI to an existing system" role. You will own the product surface of our audit product end to end — the checks that tell a customer a delivery was late or a carrier charged them incorrectly, the queue where those findings get reviewed and actioned, and the self-serve configuration that lets a customer build their own checks without us.

Audit & Visibility is becoming a standalone product. A staff engineer leads the squad and brings the backend depth; you bring the product surface. You will set the approach for it, partner directly with the squad's Product Lead on product, and be the second technical voice in the squad. You are also the day-to-day mentor for the Junior engineer joining alongside you — that is part of the job, not an extra.

Your depth is frontend and product engineering. Every engineer here is expected to be genuinely fullstack; the profile describes where your depth is, not a boundary you stay inside. You will write Django, design the API contract you consume, and reason about what BigQuery and dbt are doing underneath your screens.

What you'll work on

• The audit review queue at real scale — tens of thousands of findings per tenant, filtered, triaged, actioned and evidenced.
• Self-serve check configuration: customers building, previewing and versioning their own audit checks. "Customers configure and manage it, not IFS" is a product principle, not an aspiration.
• Explainability for incorrect-charge detection — why this charge was flagged, against which rate card, at which version — reproducible months later for a dispute.
• The Django endpoints, data contracts and pagination behaviour behind all of it.
• Where the agent sits in each of these flows: agentic audit creation, human review at minimal, meaningful points, and the guardrails that make that safe against a customer's freight spend.

What we're looking for

• Around five to eight years building and shipping production software, with real depth in React, TypeScript, state and interaction design at scale.
• You have owned a product surface end to end including the ambiguity in it — you closed the open questions rather than waiting for someone to close them for you.
• Genuinely fullstack: you write the Python/Django side, you argue about the API contract, and you know when a client-side answer is the wrong answer.
• Strong on performance and scale in the interface: pagination, virtualisation, server-side filtering, and knowing which of those the data volume actually demands.
• You build human-in-the-loop and explainability interfaces and treat auditability as a feature, not logging.
• You raise the bar on other people's work. Review, pairing and direct feedback are things you do by default, and you are happy mentoring an engineer earlier in their career.
• AI-native in practice: Cursor and Claude daily, good at giving an agent the right context, better at spotting when its output is confidently wrong.
• You form and state a view. Ambiguity is the job, not a blocker.

Nice to have

• Design-system ownership, or working shoulder to shoulder with a designer on a complex data surface.
• Django REST, BigQuery, dbt, Cube Cloud, GCP.
• LLM agents, RAG or evals in production.
• Shipped an agent that does real work, like agentic audit or fulfilment-rule automation, and the instinct to build a simulation to prove the logic first.
• Logistics, supply chain, freight, or any finance-adjacent domain where being wrong costs money.

Tech stack

React, TypeScript · Python (Django REST) · BigQuery, dbt, Cube Cloud, Superset · Pub/Sub, Datastream · GCP, Kubernetes, Terraform, GitLab CI/CD · LLMs, RAG, agent frameworks, evals · Cursor + Claude, Notion (SDD Central), Jira.

What success looks like — first 6 months

• You own the audit review and configuration surface end to end, and the open questions in it get closed by you.
• Self-serve check configuration is in production, with preview and versioning, and customers use it without us.
• The queue performs at the largest tenant's data volume, measured, not assumed.
• The Junior engineer in the squad is productive and shipping reviewed work independently, and can say what they learned from you.
• Quality standards in the squad are visibly higher: code review on all merge requests, no direct-to-prod, acceptance criteria per ticket — real, not aspirational.

Why this role exists now

Audit & Visibility is becoming a standalone product and the product surface is where its value is either visible or invisible to a customer. The Staff Engineer holds the backend and leads the squad; they cannot also be the only person setting the approach on the product side. We are shifting from feature-driven engineering to agent-led product architecture, and this is one of the surfaces where that shift has to actually land.

You'll need to be based in either the UK or the Netherlands - we're a remote team with clusters of people in London and Amsterdam.

We embrace flexibility and hybrid work opportunities to support diverse needs and lifestyles, while also valuing inclusive workplace experiences. By fostering a sense of community, we drive innovation, strengthen connections, and nurture belonging. Our commitment ensures you can work in a way that suits you best, while also engaging with colleagues to share ideas and build meaningful relationships.