At a glanceSummarised by Seekless from the posting.
Must have6
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
Skills
Next.js
Python
Node.js
PyTorch
OpenAI
Anthropic
SQL
NoSQL
Kubernetes
Docker
LLM
Full Stack Engineer – AI
Role
We 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
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Build end-to-end product features across frontend, backend, and AI integrations
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Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps.
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Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions
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Design real-time AI interactions with streaming, partial results, and tight latency constraints
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Improve system reliability, observability, and fallback mechanisms
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Collaborate closely with ML, backend, and product teams to ship features end-to-end
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Continuously iterate based on real usage and failure modes
Ideal Experiences
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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
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Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows
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Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions
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Reduce latency and improve responsiveness of AI interactions while maintaining output quality
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Build robust fallback and recovery mechanisms for LLM and tool failures in production environments
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Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring
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Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems
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Contribute to a product experience where AI feels proactive, consistent, and dependable over time