À propos de ce poste Backend Engineer, AI Systems chez Bjak
About ActAI
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 to no prompting.
Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.
Role
Build and maintain backend services and APIs for AI-powered applications
Develop inference orchestration systems that connect LLMs, application services and external tools
Design APIs for streaming responses, asynchronous processing and real-time interactions
Improve system performance through caching, batching, concurrency and database optimisation
Implement monitoring, logging, alerting and error handling for production services
Diagnose and resolve production issues involving latency, service failures and high traffic
Work with ML, frontend and mobile engineers on API design, system integration and deployment
Requirements
3+ years of backend software engineering experience building production systems
Strong programming skills in Python, Node.js or similar backend technologies
Experience designing and maintaining REST or gRPC APIs, databases and distributed services
Understanding of asynchronous processing, concurrency, caching and message queues
Experience building scalable services and optimising latency, throughput and resource usage
Experience with SQL or NoSQL databases, Docker and cloud infrastructure
Ability to debug production issues using logs, metrics and distributed tracing
Nice to Have
Experience integrating LLM APIs, inference services or AI-powered applications
Experience with model serving, streaming inference or orchestration frameworks
Experience with Kubernetes and container orchestration
Familiarity with PyTorch, vLLM or open-source model deployment
Experience building real-time communication, messaging or high-traffic
