Backend Engineer, AI (Agent Systems)
- Role
- Fullstack
- Employment
- Full-time
Open to DE only. Set where you work from to check your eligibility.
No BS summary
We build ACTAI, an AI-native productivity assistant that handles tasks and workflows conversationally. We're a high-talent-density team; you'll own the inference and orchestration layer for the product, building production systems between models and users.
Core skills
Required skills
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 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.
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
INTERVIEW PROCESS
If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.
Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.
We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.
What you'll do
- 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.
What they require
- 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.
AI Neobank (financial services) building mobile banking experiences
What people say about this company
2.6/ 5