Nicoll Curtin
Senior Python Backend Engineer (AI / LLMs) – Fully Remote
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Senior Python Backend Engineer (AI / LLMs) – Fully Remote
AI Backend Systems Engineer
We’re partnering with a tech-led business building a groundbreaking AI product focused on real-world task handling, workflow automation, and decision-making.
This is a small, high-calibre team dedicated to translating AI capabilities into reliable, structured product behaviour, prioritising execution, consistency, and real-world usability.
The product specialises in:
- Long-running workflows
- Persistent context
- Multi-step task execution
- Integration with external systems
The primary challenge is creating systems that apply AI models in predictable, structured ways, even when behaviour is non-deterministic.
About the Role
You’ll join the backend engineering team that interfaces models with the product layer. Your work will drive:
- End-to-end execution of AI workflows
- Consistent API provisioning for AI features
- Reliable system performance under real-world usage conditions
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Your responsibilities may include:
- Designing and optimising inference pipelines and orchestration engines
- Developing AI-driven backend services
- Addressing latency, throughput, batching, and caching for performance
- Implementing monitoring, logging, and robust production reliability safeguards
Key Responsibilities
You’ll focus on:
- AI infrastructure design and operation – ensuring seamless flow from model to product
- Production-scale system maintenance – debugging, incident response, and scaling
- Backend optimisation – balancing speed, reliability, and efficiency
- Cross-team collaboration – ensuring AI systems comply with product and reliability expectations
What They Value Most
This isn’t just about technical familiarity—experience in real production environments is critical. Key priorities include:
- End-to-end ownership: Proven ability to design, implement, and sustain backend systems in production.
- AI system maturity: More than helper integrations—practical experience with AI’s operational quirks.
- Real-world challenges: Hands-on experience tackling:
- Latency and throughput optimisation
- Monitoring and observability
- Debugging and incident resolution
- Scaling systems under load
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Technical Environment
- Primary language: Python
- Secondary: Node.js
- Databases: SQL & NoSQL
- AI ecosystems: OpenAI, Anthropic, and similar
- Containerisation: Docker & Kubernetes
- Cloud: Flexibility with AWS, Azure, or GCP (platform focus is secondary to system expertise)
Team & Culture
- Fully remote-first: Work globally, collaborate asynchronously.
- Small, elite team: High standards, high ownership, high impact.
- Pragmatic problem-solving: Evolutionary systems demand typeable trade-offs.
- Ownership mindset: Not just code delivery—operational excellence matters equally.
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