Senior ML/AI Solutions Engineer

About the role

Moonfare is hiring a Machine Learning / AI Solutions Engineer to bridge the gap between business needs and AI capabilities, focusing on leveraging existing tools and platforms to deliver intelligent automation and AI-native experiences. This is a high-impact role focused on strategic solution design, tool standardisation, and business enablement, helping Moonfare transition into an AI-augmented fintech platform.

What you'll do

• Serve as Moonfare's internal AI subject matter expert, staying current with emerging AI trends, tools, and capabilities to advise leadership on opportunities.
• Act as the primary liaison between business units and technical teams, translating complex operational challenges into actionable AI solution requirements.
• Design and implement end-to-end AI solutions and agentic workflows using low-code/no-code platforms and vendor tools (e.g., AWS Bedrock, Camunda, n8n, Vercel v0, REALM), focusing on rapid prototyping and deployment.
• Evaluate, select, and integrate commercial and cloud-native AI services (e.g., AWS Bedrock, OpenAI, Anthropic) to maximise business value and efficiency.
• Establish and maintain the company-wide AI Tooling Governance framework, defining approved use cases and standardised tools to prevent tool sprawl and ensure compliance.
• Educate and communicate the potential, limitations, and mechanics of LLMs and AI technologies to non-technical stakeholders in clear, business-focused language.
• Work closely with Security and Compliance teams to ensure the secure, ethical, and compliant use of generative AI solutions in a regulated environment.
• Drive solution performance evaluation and tracking (e.g., quality, cost, and business impact/ROI).

What you'll need

• Bachelor's degree in Computer Science, Engineering, Business, or related field; or equivalent practical experience.
• 5+ years of experience in technology or solutions architecture roles, with 2+ years focused on designing and implementing AI/LLM-based solutions.
• Deep theoretical understanding of how LLMs work, RAG architectures, and agentic workflows, and how they apply to business automation.
• Proven experience evaluating, selecting, and implementing vendor-based AI solutions and workflow orchestration tools (e.g., AWS Bedrock, Camunda, n8n, Vercel v0, REALM, or similar low-code/no-code platforms).
• Exceptional communication skills with the ability to articulate complex technical concepts (AI/LLMs) to executive and non-technical audiences.
• Demonstrated ability to identify, scope, and prioritise business problems suitable for AI intervention, focusing on measurable ROI.
• Strong familiarity with AI risk, safety, and governance frameworks, and experience establishing tooling standards in a corporate environment.