Full Stack Engineer, AI systems

CompanyA1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows.

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.

RoleWe are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users.

Focus

• Build end-to-end product features across frontend, backend, and AI integrations
• Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps.
• Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions
• Design real-time AI interactions with streaming, partial results, and tight latency constraints
• Improve system reliability, observability, and fallback mechanisms
• Collaborate closely with ML, backend, and product teams to ship features end-to-end
• Continuously iterate based on real usage and failure modes

Ideal Experiences

• Strong experience in full stack engineering (frontend + backend)
• Solid understanding of system design and API architecture
• Experience working with LLMs, RAG systems, or AI-powered applications
• Ability to handle ambiguity and make pragmatic engineering decisions
• Strong ownership - able to take features from idea to production
• Comfort working in fast-moving environments with evolving requirements

Outcomes

• Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows
• Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions
• Reduce latency and improve responsiveness of AI interactions while maintaining output quality
• Build robust fallback and recovery mechanisms for LLM and tool failures in production environments
• Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring
• Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems
• Contribute to a product experience where AI feels proactive, consistent, and dependable over time

Tech Stack

• Next.js
• Python
• NodeJs
• Pytorch
• OpenAI / Anthropic / open-source LLMs
• SQl & noSQL
• Kubernetes
• Docker