Innovation Engineer

About the role

TJDEED is looking for an Innovation Engineer to build and run its Innovation Engine, turning business, customer and market problems into experiments, prototypes, AI-enabled solutions and validated opportunities. The role works closely with Technology teams and spans research, prototyping and handover to production.

Key responsibilities

• Identify opportunities and pain points from customers, Sales, Services, Technology and internal operations.
• Turn problems into innovation hypotheses, solution concepts and technical experiments.
• Research emerging technologies, especially AI agents, LLMs, automation, integrations and enterprise software.
• Build prototypes and proofs of concept (POCs) to validate technical feasibility and business value.
• Evaluate build-vs-buy options, APIs, platforms, models and third-party technologies.
• Work with Technology teams on architecture, security, integration and scalability.
• Document experiments, assumptions, results, lessons learned and recommendations.
• Maintain an Innovation Pipeline covering ideas, experiments, POCs and validated opportunities.
• Help transfer successful innovations into production or commercialisation.
• Use AI-assisted development and automation to speed up experimentation.

Required profile

• Strong curiosity about emerging technology and a builder mindset, preferring prototypes over theoretical proposals.
• Comfortable with rapid experimentation and learning from failure.
• Ability to communicate technical concepts clearly to non-technical stakeholders and collaborate across Technology, Sales, Services, AI and other units.
• 2-5 years in software, AI, R&D, automation or related technology; a Bachelor's degree in Computer Science, Software Engineering, AI, IT or a related field preferred.
• R&D, POC, hackathon or innovation experience is a strong advantage.

Required skills

• Software engineering fundamentals.
• Practical Python and/or JavaScript/TypeScript.
• APIs, REST, webhooks, databases and integrations.
• Generative AI, LLMs, AI agents, prompt engineering and AI workflows.
• Cloud platforms, automation tools, Git and modern development practices.
• Ability to assess security, reliability and scalability at prototype level.