About Us
Resaro was founded on the belief that AI will change the world in ways we cannot even imagine, but every new technology needs safeguards to advance.
Resaro builds custom AI testing software that helps organisations validate the performance, robustness, and safety of mission-critical AI systems — spanning computer vision, generative AI, and autonomous systems. Our clients include government, military, and commercial organisations deploying AI in high-stakes environments. We work through embedded teams deployed on-site or closely integrated with our clients.
We're looking for an AI Engineer to build models and model-based features, and integrate AI into our product. This role is more implementation-focused than research-focused, but research is part of the job. You'll work across model development, evaluation methodology, synthetic data generation, MLOps, and agentic systems. Depending on your strengths, you may weight toward LLM, computer vision, reinforcement learning, synthetic data, agentic AI, or robotics/edge AI.
What You'll Do
- Build, deploy, and maintain AI models and model-based features in production — from fine-tuning through serving and monitoring.
- Design and implement evaluation methodologies, test plans, and quality frameworks for AI systems (LLM, CV, RL).
- Build agentic AI systems using frameworks like CrewAI, LangGraph, LlamaIndex, and MCP.
- Create synthetic data generation pipelines, adversarial test cases, and benchmark datasets.
- Own model serving infrastructure, MLOps pipelines, and deployment automation.
- Research model behaviour, track global SOTA across domains, and translate findings into product improvements.
- Package and deploy AI capabilities for customer environments, including edge and air-gapped deployments.
- Contribute to the technical differentiation that makes Resaro's testing products industry-leading.
What We're Looking For
- 3–7 years of experience in AI/ML engineering, with production deployment experience.
- Strong Python skills and experience with ML frameworks (PyTorch, TensorFlow, or similar).
- Demonstrated depth in at least one domain: LLM/NLP, computer vision, reinforcement learning, or synthetic data generation.
- Experience deploying models to production: model serving, API integration, monitoring, versioning.
- Understanding of evaluation methodology: experimental design, statistical rigour, benchmark creation.
- Ability to bridge research and engineering — read papers, prototype approaches, and ship production code.
- Clear communicator who can explain AI concepts to non-specialist colleagues and customers.
Nice to Have
- MSc or PhD in AI/ML, Computer Science, or related field.
- Experience with LLM fine-tuning, RLHF, or prompt engineering at production scale.
- Computer vision expertise: adversarial ML, deepfake detection, AIGC evaluation.
- Reinforcement learning experience: reward modelling, policy evaluation.
- Experience with ROS2, edge AI, simulation (Gazebo/Isaac Sim) for robotics applications.
- Published research or contributions to open-source AI projects.
- Experience working in defence, pharma, or other regulated industries.
- Experience with air-gapped or on-premise deployment environments.
Our Hiring Blueprint
We have deliberately tightened our hiring bar. Every engineer we bring on must meet these standards:
- Production-grade code: You must demonstrate the ability to write clean, tested, maintainable code that ships to production — not just prototypes or notebooks.
- Communication and handover: You must be able to clearly document your work, hand over context to colleagues, and communicate technical decisions to non-technical stakeholders.
- Independent operation: You must be able to work autonomously with minimal supervision, manage ambiguity, and make sound technical decisions.
- T-shaped profile: Depth in your primary area plus working knowledge across the stack. We value breadth alongside expertise.
