Job Details

We're supporting a Series B company building AI-native software to modernise how businesses operate and make decisions. Their platform combines large language models, workflow automation, and structured data systems to deliver intelligent product experiences that users rely on daily.

They're looking for an AI Engineer focused on applied AI systems, someone who can turn foundation models into reliable, production-grade product capabilities. This role is focused around real-world implementation: prompt engineering, retrieval systems, orchestration, evaluation, and backend integration rather than model research or training frontier models from scratch.

You'll work closely with engineering, product, and design teams to ship AI features that are fast, trustworthy, and deeply integrated into the product experience.

What You'll Do

• Design and build LLM-powered product features using prompt engineering, structured outputs, tool usage, and function calling
• Develop and improve retrieval-augmented generation (RAG) systems, including chunking strategies, embedding pipelines, indexing, and retrieval optimisation
• Build orchestration layers for multi-step AI workflows involving agents, tools, memory, retries, and fallback handling
• Integrate AI systems into backend services and production infrastructure
• Evaluate model quality, latency, hallucination rates, and operational cost, then iterate to improve performance and reliability
• Collaborate cross-functionally with product and design to create intuitive AI experiences with strong UX considerations
• Experiment with emerging AI tooling, frameworks, and open-source models to improve product capabilities
• Help define engineering best practices around observability, testing, evaluation, and safety for AI systems

What We're Looking For

• 3+ years of experience building AI-driven products, ML-enabled applications, or backend systems with AI integrations
• Strong hands-on experience working with modern LLM APIs and ecosystems (OpenAI, Anthropic, open-source models, etc.)
• Solid Python engineering skills and experience building production backend services
• Experience designing prompts, structured outputs, and tool-based workflows for real-world applications
• Familiarity with retrieval systems, embeddings, vector databases, and semantic search concepts
• Strong product instincts with attention to usability, responsiveness, and reliability
• Comfortable operating in fast-moving startup environments with high ownership and ambiguity

Nice to Have

• Experience building agentic systems with planning, tools, and memory
• Familiarity with frameworks such as LangChain, LlamaIndex, DSPy, or similar orchestration tooling
• Exposure to fine-tuning, adapters, evaluation pipelines, or synthetic data generation
• Experience deploying AI workloads in cloud environments and monitoring production inference systems
• Background working on workflow automation, productivity software, or AI copilots