Backend Engineer, AI Systems

ActAI · Ireland · 2 weeks ago

Category: Backend

ai anthropic api docker embeddings llm machine-learning nodejs nosql observability openai python pytorch sql

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.

Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations

Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things

Role

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. 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 and orchestration layers that handle multi-step workflows, tool calls, and retries
Manage the full lifecycle of AI requests: routing, caching, batching, streaming, and state management
Optimize latency, throughput, and cost across model inference and downstream systems
Design systems that remain reliable despite non-deterministic model behavior and external dependencies
Implement observability for AI systems, including logging, tracing, and debugging of model outputs and failures
Collaborate with ML and product teams to translate model capabilities into stable, production-grade APIs

Ideal Experiences

Experience running high-throughput, low-latency services.
Familiarity with AI inference patterns (LLMs, embeddings, multimodal).
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.
Multi-step AI workflows complete successfully across tools and services, with robust handling of failures and retries
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.
System design evolves to support increasing scale, complexity, and new AI capabilities without major rewrites.
Python
NodeJs
Pytorch
OpenAI / Anthropic / open-source LLMs
SQl & noSQL
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.