Backend Engineer, AI (Agent Systems)

BJAK
Greater London, UK
3 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Software Debugging Distributed Systems Python (Programming Language) Node.Js NoSQL Open Source Technology SQL Databases Data Logging Pytorch Large Language Models
+6 more
Backend Kubernetes Low Latency Machine Learning Operations Front End Software Development Docker

Job description

About the Role

A1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior.

As a Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. Your work sits between models and users, where latency, correctness, reliability, and cost directly impact real-world experience. You will build and operate production systems that turn model capability into fast, stable, observable APIs used across mobile and desktop clients.

Focus

  • Build and operate backend systems that serve AI-powered features in production.
  • Design inference pipelines, orchestration layers, and service boundaries around models.
  • Own production concerns: monitoring, logging, alerting, and incident response.
  • Optimize latency and throughput across inference, caching, batching, and streaming.

Ideal Experiences

  • Strong backend engineering fundamentals in production environments.
  • Experience running high-throughput, low-latency services.
  • Familiarity with AI inference patterns (LLMs, embeddings, multimodal).
  • Comfortable debugging distributed systems under load.
  • Bias toward shipping and learning from production behavior.

Outcomes

  • Backend systems run reliably at scale, handling production AI traffic with low latency and high throughput.
  • APIs are stable, clear, and support seamless integration with frontend and ML systems.
  • Production incidents are quickly detected, diagnosed, and resolved, minimizing user impact.
  • Iterative improvements based on real usage continuously increase system performance and reliability.

Tech Stack

  • Python
  • NodeJs
  • Pytorch
  • OpenAI / Anthropic / open-source LLMs
  • SQL & noSQL
  • Kubernetes
  • Docker

How We Work

The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product.

Requirements

  • Strong backend engineering fundamentals in production environments.
  • Experience running high-throughput, low-latency services.
  • Familiarity with AI inference patterns (LLMs, embeddings, multimodal).
  • Comfortable debugging distributed systems under load.
  • Bias toward shipping and learning from production behavior., * Backend systems run reliably at scale, handling production AI traffic with low latency and high throughput.
  • APIs are stable, clear, and support seamless integration with frontend and ML systems.
  • Production incidents are quickly detected, diagnosed, and resolved, minimizing user impact.
  • Iterative improvements based on real usage continuously increase system performance and reliability.

Tech Stack

  • Python
  • NodeJs
  • Pytorch
  • OpenAI / Anthropic / open-source LLMs
  • SQL & noSQL
  • Kubernetes
  • Docker

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Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

1:24 min

Building client-facing AI agents for engineering teams

Alfonso Graziano Alfonso Graziano · Coffee With Developers

2:37 min

Comparing traditional SQL tables versus NoSQL non-tabular databases

Stanimira Vlaeva · JS Congress

2:07 min

Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · World Congress 2025

1:52 min

Structuring and scaling the backend engineering team

Stefan Lingler Stefan Lingler +1 · Coffee With Developers

1:50 min

Overview of the Edge AI ecosystem and tech stack

Maxim Salnikov Maxim Salnikov · World Congress 2025

3:16 min

Terminology differences between relational and NoSQL databases

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