Full Stack Engineer

About Akerio. Akerio is a UK-based applied AI company. We build platforms that turn messy enterprise data into scored, auditable recommendations: the next diesel price for one of Europe's largest independent fuel distributors, a risk flag on a tenant across 41 shopping centres, a credit score a bank's committee can trace back to the source record. Our models are built by a PhD team from Stanford and the University of Chicago, every output shows what drove it and how confident it is, and the platform runs on-premise or in the cloud. We are live in production across energy, commercial real estate and financial services, with 18 AI modules running in one deployment alone. We are a small team shipping to demanding enterprise clients, so the work you do reaches production and gets used. • Job: Full time • Job type: Remote The role Our largest deployment, a retail property analytics platform, is live across 41 shopping centres. Asset managers, leasing and finance teams open it to see footfall, turnover forecasts, tenant risk and NOI, then ask the built-in AI assistant why a number moved. Behind it sit 49 dashboard widgets, 53 AI tools, a set of NestJS microservices and a Python compute service. You take features across that whole path: the React screen, the NestJS endpoint, the Postgres query, and the AI commentary that explains the result. You will also talk to the people using it, because most of our backlog comes straight from their feedback. What you will work on • Dashboard widgets for leasing, finance and visitor analytics: time-series charts, forecast versus actual, filters that live in the URL so a view can be shared. • The customizable home dashboard: drag, resize and save widget layouts per user. • NestJS microservices: core business APIs, notifications, and the AI service that runs LLM tool calls and talks to our tools over MCP. • Data endpoints over PostgreSQL and PostGIS, with SQL migrations you write and own. • Wiring Python forecasting and risk models (FastAPI compute service, scheduled by Airflow) into the product. • AI features in the UI: chat, quick summaries and per-widget AI commentary, cached in Redis and generated in the user's language. • Production quality: health checks, logging, dependency upgrades, and closing security findings before they become incidents. Our stack Frontend: React, TypeScript, Vite, MUI, Tailwind, shadcn/ui, Zustand, React Router, ApexCharts, React Grid Layout, i18n (Turkish and English) Backend: Node.js, NestJS microservices in a Yarn workspace monorepo, gRPC, Kafka, Redis, OpenSearch, Socket.IO Data: PostgreSQL 17 with PostGIS, SQL migrations Compute and ML Python, FastAPI, Airflow for scheduled forecast and scoring runs AI: Anthropic Claude via API, tool calling and MCP, RAG over client data, Langfuse for monitoring conversations and quality Infra: Azure, Docker and Docker Compose, nginx, Jenkins, Bitbucket, OpenTelemetry and Jaeger, Trivy; Kubernetes (Rancher) on the roadmap How we build: Jira, Figma, spec-first work with AI coding tools (Claude Code), code review on every PR You need to be strong in TypeScript on both sides. The rest you can pick up here. Your first six months • First month: local setup running, first pull request merged in week one, one widget or endpoint shipped to production. • Month three: you own one product area end to end (for example finance analytics) from Figma to production. • Month six: you shape API and frontend standards across services and pair with juniors on their first features. What you bring • 4+ years building production web applications, with real depth in TypeScript, React and Node.js. • NestJS or a similar structured Node framework, REST API design, and PostgreSQL schema and query work. • You have shipped data-heavy UIs: charts, big tables, filters, exports, and you know how to keep them fast. • Docker, CI pipelines and debugging in a deployed environment, not only on localhost. • You write tests and care about code that the next person can read. • Clear written English. Turkish is a plus: most of our current users work in Turkish. Nice to have • Python and FastAPI, enough to read and extend a compute endpoint. • Microservice plumbing: Kafka, gRPC, Redis caching, tracing. • You have built a UI on top of LLM output: streaming, citations, tool calls, human review. • Experience with real estate, retail, energy or finance products. How we work Small team, short feedback loops. Client feedback lands in Jira every week, we write the spec before the code, and every pull request gets a review. We use AI coding tools as standard and still expect you to own every line you merge. Hiring process 1. 30-minute intro call. 2. 30-minute session: present your case to the CPO and an engineer. 3. 15-minute call with a founder. What we offer • Fully remote, with overlap on UK and Türkiye working hours.