AI Engineer

• Own AI features end-to-end from designing agentic workflows and RAG pipelines to the infrastructure that runs them in production at scale
• Work on genuinely hard problems financial data is structured, high-stakes and unforgiving, making it one of the more interesting domains to apply LLMs to
• Build the evaluation frameworks and experimentation loops that turn good models into reliable, production-grade systems
• Partner directly with product and domain experts to push the frontier of what AI can do inside an ERP, not just what's been done before

Senior AI Engineer

• Build and productionize multi-agent workflows on Anthropic/OpenAI APIs: orchestration, tool use, structured outputs, guardrails.
• Design hybrid retrieval architectures that combine knowledge graphs, vector search, and ranking into a single coherent context layer.
• Build evaluation harnesses and observability for agent behavior — quality, latency, cost — and use them to drive iteration.
• Integrate LLM inference, retrieval, and reasoning services into production backends.
• Work with researchers and domain experts to turn neuro-symbolic prototypes into robust product features.

• Develop intelligent models that personalize digital therapies.
• Work with real-world data from SaMDs to design machine learning pipelines that predict health risks.
• Build scalable data pipelines for model training, testing, and deployment.
• Work closely with engineering to embed models into production SaMDs.
• Ensure models meet explainability, reproducibility, and regulatory standards.
• Evaluate generative AI tools for education and coaching applications.

AI/ML Engineer

• Design, develop, and optimize applications leveraging internal LLM platforms
• Create, test, and maintain high-quality prompts to drive accurate and predictable outputs
• Apply advanced prompting techniques including: Few-shot prompting, Prompt chaining, RAG (Retrieval-Augmented Generation)
• Build full-stack, end-to-end GenAI applications
• Design and implement backend services integrating LLMs with enterprise systems
• Develop or collaborate on web-based user interfaces
• Integrate GenAI capabilities into existing applications and workflows
• Analyze structured and unstructured data for GenAI use cases
• Translate business requirements into LLM-driven workflows
• Document design decisions, prompts, and system behavior clearly

Generative AI Developer

• Take complete ownership and deliver major AI engineering features within agreed timelines.
• Own AI output quality, structure, and predictability across all user-facing AI interactions.
• Design, implement, and maintain output-type-based AI systems, including segmentation, routing, and enforcement.
• Ensure consistent output structure and formatting across different LLMs for the same request type.
• Integrate and orchestrate multiple LLM providers via OpenRouter, managing model selection, fallback strategies, and cost optimisations.
• Design and orchestrate tool-using and agentic AI workflows, defining clean tool contracts (including MCP-based tools), function-calling interfaces, and reliable AI-to-system integrations.
• Build and maintain complex, multi-step LLM workflows, including with orchestration frameworks such as LangChain or LlamaIndex, for advanced reasoning, context reuse, and retrieval.
• Design and manage production prompt systems with dynamic prompting, context injection, and conditional logic.
• Own the deployment and release of LLM experiments, prompt management, and Langfuse-based evaluation pipelines.
• Run A/B tests across models, analyse results, and present data-driven impact assessments of AI features and experiments.
• Monitor AI system metrics, quality signals, latency, and release health using Langfuse and other observability tools.
• Deep-debug complex LLM chains using Langfuse traces, identifying bottlenecks and optimising for cost, latency, and context-window usage, and build output-scoring systems to root-cause hallucinations and logic errors.
• Write clean, scalable, and maintainable TypeScript code across the Next.js and Node.js stack.
• Build reliable backend logic for AI systems, with strong error handling, request validation, fallback flows, and predictable behaviour in production, including reliable tool execution and AI-to-service integrations.
• Ensure high code quality through testing, code reviews, and clear engineering standards.
• Monitor, troubleshoot, and improve production performance, reliability, and system health.
• Drive maintainability and technical quality through solid architecture, refactoring, and disciplined release practices.

• Implement AI-powered product features and workflows based on specifications from senior engineers.
• Write, iterate on, and manage production prompt templates following established patterns for dynamic prompting and context injection.
• Participate in prompt experimentation workflows by drafting variants, running A/B tests across models via OpenRouter, and documenting results.
• Integrate structured output schemas (JSON mode, function calling, Zod/JSON schemas) to ensure AI responses are predictable and application-ready.
• Build and maintain output enforcement mechanisms such as validators and repair loops under senior guidance.
• Help implement tool calling and function calling integrations so AI features can fetch data or trigger actions, following patterns established by senior engineers.
• Contribute to evaluation pipelines and help assess prompt and model quality using Langfuse.
• Write clean, maintainable TypeScript code within the Next.js and Node.js stack.
• Participate in code reviews and incorporate feedback.
• Document technical decisions and system behaviour to support knowledge sharing.
• Collaborate with product, growth, data, and billing teams to deliver features on time.

Junior AI Engineer

• Develop solutions to integrate and optimize language models within the botario platform
• Monitor trends and advances in NLP and AI research
• Deploy and operate LLMs in production on the botario platform for customers
• Experiment with new models and architectures
• Evaluate LLMs with respect to response quality, latency, and cost