AI Engineer

We are hiring a AI Engineer to join our Customer Success team. This is a hands-on, customer-facing engineering role focused on bridging the gap between an AI prototype and a production-ready solution.

You will work directly with prospective and existing customers, from early proof-of-concept through pilot deployment, building AI agents and applications on the RE:AI platform. This is not a pure research or pre-sales role — you will write code, build solutions, solve real customer problems and turn early-stage prototypes into reliable solutions that work with real data, users and production requirements.

Responsibilities

• Partner with Customer Success and Sales teams to understand customer needs, scope technical solutions, and develop proof-of-concepts and working prototypes to support customer engagements and pilot conversions.
• Design, build and deploy AI agents and applications on the RE:AI platform, leveraging the Model Gateway, MaaS model catalog and GPU-as-a-Service platform.
• Build agentic solutions incorporating capabilities such as tool calling, retrieval, orchestration, guardrails and human-in-the-loop workflows, tailored to customer use cases.
• Transform early-stage or rapidly developed prototypes into production-ready solutions, incorporating appropriate error handling, observability, access controls, security, reliability and cost optimisation.
• Own the technical implementation of customer pilots end to end, including development, system and data integration, deployment, troubleshooting, performance tuning and technical support.
• Work directly with customer engineers, architects and stakeholders to explain solution designs, technical decisions and trade-offs clearly.
• Act as a technical escalation point during customer pilots, diagnosing and resolving issues to support successful deployment and adoption.
• Identify common requirements and patterns across customer engagements and develop reusable components, tools and solution patterns for future customer implementations.
• Collaborate closely with internal Product, Engineering, Customer Success, Sales and Delivery teams, bringing customer feedback and technical insights back into the platform.
• Support customer engagements on-site and travel where required based on project and business needs.

Requirements

• Strong hands-on software engineering experience, with demonstrated ability to take prototypes or early-stage applications through to production-ready deployment.
• Experience building AI/LLM-powered applications or agentic systems, including areas such as tool calling, retrieval/RAG, orchestration, guardrails and workflow integration.
• Proficiency in at least one backend programming language or technology stack, with a good understanding of APIs, application integration and cloud infrastructure.
• Practical understanding of LLM behaviour, prompting and evaluation, including performance, latency and cost considerations in a production environment.
• Experience using modern AI-assisted development and coding tools, with the ability to review, improve and productionise AI-generated code.
• Good understanding of production engineering practices, including authentication and access control, monitoring, error handling, security, reliability and performance optimisation.
• Strong customer-facing and communication skills, with the ability to engage directly with customer engineers, architects and senior stakeholders.
• Comfortable working independently in ambiguous and fast-moving environments, defining technical scope and managing multiple customer engagements.
• Willingness to travel and work on-site with customers where required.
• Experience in forward deployed engineering, solutions engineering or startup/generalist engineering environments would be advantageous.
• Experience delivering solutions within regulated industries, such as financial services, healthcare or government, including exposure to compliance, security and data-residency requirements, would be advantageous.
• Experience with LLM gateways, model routing, guardrail/policy systems or internal model-serving platforms would be advantageous.
• Demonstrated ability to turn customer-specific solutions into reusable technical components and patterns that can be applied across multiple customer engagements.