Auxilius.ai

Artificial Intelligence

• Collaborate with the team to find strong solutions to new types of problems in an AI-first architecture
• Drive a DevOps-style approach across the full specification → development → delivery lifecycle (inspired by the DevOps Handbook)
• Propose and implement approaches to make delivery faster and more reliable (e.g., improving flow, feedback loops, and recovery)
• Implement and evolve monitoring/observability across systems (metrics, logs, traces, alerting) so we can detect issues early and learn from production
• Coach and collaborate with junior developers on good development, design and architecture practices
• Collaborate with the team to develop an AI-driven development process from feature specs down to production deployment
• Plan & document backend architecture in the Arc42 framework
• Extend & communicate the domain model using DDD principles
• Manage and extend application infrastructure on Azure with Pulumi

Application Architect – AI Native Startup

AI Engineer

• Own the LLMOps pipeline: Evaluate infrastructure, prompt optimization loop, and the production integration that turns experiments into reliable customer-facing features
• Design evaluation strategy per output type: Decide when to use deterministic evals (exact match, schema validation, embeddings) vs. LLM-as-judge, and build the rubrics, test datasets, and human-review loops that make the system trustworthy
• Drive prompt engineering and optimization across all LLM operations in the product: Moving from hand-tuned prompts to a measurable, iterative process
• Pick the right tool for each problem: Some things are LLM problems, some are embedding + classical NLP problems, some are deterministic logic
• Run the production side of AI features: Observability (Langfuse /LangSmith / similar), cost and latency engineering, incident response when an LLM feature degrades
• Build human-in-the-loop workflows: Review queues, feedback ingestion, labeling; so production signal feeds back into evals and prompt iteration
• Mentor our AI & Analytics Intern and contribute to how we build the AI team over time

AI Engineer – LLM Ops & Evaluation

Full-stack Engineer

• As a Senior Engineer you will be one of the first full-time engineer and collaborate closely with our Co-Founder and CTO, James
• In the first 6-12 months you will have successfully: built AI Agents to help our users build risk-controls, data analytics and reports
• extended the frontend to include new pages, components and features using HTML, CSS and Typescript (Angular/React)
• extended backend applications based on Java/SpringBoot to include new services, components and entities
• apply context engineering, tool use and evaluations with many frontier LLMs
• implement AI features on the frontend
• follow DDD for application design, use TDD to guide through implementing new features, use SOLID principles and clean code
• deploy applications to Kubernetes using Helm and Flux-v2 and manage infrastructure in Azure using Pulumi
• support architecture design by either contributing or challenging decisions to find a robust architecture that is a pleasure to maintain and document architecture decisions and rationale