AI Forward Deployed Engineer

Role Overview

AI Forward Deployed Engineer at Network International, based in Dubai, United Arab Emirates. Grade: Senior Manager. Reports to AI Project Portfolio Lead.

Company Overview

Network International is the largest financial technology company in the Middle East and Africa. Payments is the core business, with services across more than 50 countries including the UAE, Jordan, South Africa and Egypt. In addition to payments, the company offers solutions across data and insights, lending, insurance and risk management. Customers range from businesses of every scale and segment to growing direct-to-consumer card offerings.

The company fosters a culture where trust, accountability and achievement go hand in hand. Work is characterized by innovation, agility and purposeful collaboration, with investment in people to make bold decisions, learn fast and develop expertise alongside industry leaders.

Role Purpose

The AI Forward Deployed Engineer embeds directly into priority enterprise AI projects—with particular focus on AI Champion-led initiatives—to turn approved solution designs into working, production-ready applications, agents, automations and integration flows. The role provides hands-on engineering leadership across the full build lifecycle, from technical design and architecture decisions through prototyping, testing, deployment and production handover, ensuring solutions are secure, scalable and reusable rather than one-off demos.

Key Responsibilities

Implementation & Delivery

• Embed into selected AI Champion and business-unit projects to implement approved solution designs as working technical components, applications, agents, automations and integration flows.
• Design, prototype, build, configure and test AI applications, copilots, agents, automations and orchestration workflows using approved enterprise platforms and technologies.
• Develop solution prototypes and production-ready components using advanced prompting, front-end and back-end coding, low-code tools, APIs and cloud-based AI services.
• Prepare and execute functional testing, integration testing, user acceptance testing, model evaluation, red-teaming and guardrail testing for selected solutions.
• Monitor early production performance and support the resolution of technical issues following deployment.

Architecture & Technical Decision-Making

• Make and enforce sound technical, integration and architecture decisions so solutions are secure, scalable, maintainable and reusable across the organisation.
• Validate, pressure-test and enhance solution designs at implementation, feeding practical build learnings back to the AI Portfolio & Solutioning Lead.
• Select appropriate AI, integration and automation patterns based on business requirements, technical feasibility and operational constraints.
• Implement retrieval-augmented generation, tool use, workflow orchestration and other appropriate AI engineering patterns based on solution requirements.
• Apply working machine learning knowledge to operate AI tooling, run Python and evaluate model outputs; hands-on model building is appreciated but is not the core focus of the role.

Enablement & Coaching

• Coach AI Champions hands-on in designing and building data pipelines, agentic workflows, automations and software applications while reinforcing approved technical and delivery standards, keeping them in the driver's seat.
• Stay ahead on emerging AI technologies, tools and frameworks for building applications and agentic workflows, and schedule internal sessions to teach and upskill AI Champions.
• Provide practical coaching and constructive technical guidance without unnecessarily taking ownership away from AI Champions.
• Support AI Champions and project teams in defining test scenarios, acceptance criteria, evaluation datasets and quality thresholds.

Reusable Assets & Standards

• Build and maintain reusable prompt templates, skills, tools, workflows, code components and reference implementations for organisation-wide use.
• Identify opportunities to improve solution quality, engineering standards, developer experience, reuse and delivery speed across the AI transformation program.
• Identify reusable components and common technical patterns that reduce duplication and accelerate future delivery.

Integration & Security

• Integrate AI solutions with approved enterprise data sources, APIs, applications, identity services, hosting environments and AI Factory capabilities.
• Apply approved security, privacy, responsible AI and engineering controls throughout development, testing and deployment.
• Apply working understanding of software architecture, application security, identity and access management, data privacy and secure development practices, with the judgment to make architecture decisions at implementation.

Technical Operations & Troubleshooting

• Diagnose and resolve technical issues involving prompts, agents, APIs, integrations, authentication, data access, environments and platform configurations.
• Diagnose technical issues methodically and drive problems through to practical resolution.
• Maintain clear technical documentation covering solution components, configurations, interfaces, deployment procedures, testing results and operational dependencies.
• Track technical requests, defects, integration needs and deployment dependencies, ensuring clear ownership and timely follow-through.
• Coordinate with Technology teams on code review, source control, release management, environment promotion, production deployment and operational handover.

Stakeholder Communication & Reporting

• Provide clear technical updates, risks and recommendations to the AI Portfolio & Solutioning Lead and relevant project stakeholders.
• Communicate complex technical concepts clearly to business users, AI Champions and nontechnical stakeholders.
• Contribute to a collaborative, accountable and performance-driven engineering culture focused on secure delivery and measurable business value.

Qualifications & Experience

• Extensive hands-on experience in software engineering, AI engineering, application development, automation or a related technical role, including experience operating as an embedded or forward-deployed engineer alongside delivery teams.
• Bachelor's degree in computer science, software engineering, information technology, data science or a related discipline, or equivalent professional experience.
• Demonstrated experience designing, developing, testing and deploying enterprise applications, integrations or AI-enabled solutions into production.
• Practical experience with generative AI, large language models, copilots, AI agents, prompt engineering, retrieval-augmented generation and workflow orchestration.
• Strong programming capability in Python, JavaScript, TypeScript, C# or another relevant enterprise development language.
• Working machine learning knowledge sufficient to operate AI tooling, run Python and evaluate models; practical model-building experience is appreciated but not essential.
• Experience working with APIs, software development kits, web services, authentication mechanisms, structured data and enterprise system integrations.
• Familiarity with cloud AI services, enterprise AI platforms, agent development frameworks, automation tools and low-code development environments.
• Experience with version control, code review, testing frameworks, continuous integration and deployment practices.
• Experience preparing technical designs, interface specifications, test plans, deployment documentation and operational handover materials.
• Familiarity with model evaluation, prompt testing, red-teaming, guardrails, content safety and responsible AI controls is highly desirable.
• Experience supporting nontechnical or citizen developers in building controlled applications and automations is advantageous.
• Experience within financial services, fintech, payments or another regulated environment is preferred.
• Ability to perform with pace, accuracy and sound judgment in a fast-moving, commercially focused environment.

Skills & Competencies

• Demonstrate strong hands-on engineering, coding, configuration and technical problem-solving capability.
• Convert approved solution designs into clear, maintainable and testable technical implementations, making pragmatic architecture decisions along the way.
• Understand the strengths and limitations of large language models, agents, retrieval systems, workflow automation and traditional software components.
• Write clear, secure and maintainable code and apply appropriate development, testing and documentation standards.
• Maintain strong attention to detail across code, configurations, data flows, access controls, testing and release activities.
• Communicate complex technical concepts clearly to business users, AI Champions and nontechnical stakeholders.
• Provide practical coaching and constructive technical guidance without unnecessarily taking ownership away from AI Champions.
• Collaborate effectively as a peer with the AI Portfolio & Solutioning Lead, data scientists, architects, platform teams, developers and Information Security stakeholders.
• Balance rapid prototyping with the engineering discipline required for scalable and production-ready solutions.
• Plan and prioritise effectively across multiple use cases, technical requests and delivery dependencies.
• Take clear ownership of technical deliverables, defects and dependencies and communicate risks early.
• Learn new models, platforms, frameworks and development practices quickly and share that knowledge through practical enablement of AI Champions.

Additional Information

Success in this role is demonstrated by: approved AI solution designs implemented as working technical components and applications with clear traceability to business requirements; sound architecture and integration decisions ensuring solutions are secure, scalable, maintainable and reusable; selected AI applications, agents and automations delivered to agreed quality, security, performance and timeline expectations; prototypes developed quickly without compromising controls required for pilot and production deployment; AI Champions receiving practical hands-on technical support and becoming increasingly capable of building high-quality solutions independently; internal enablement sessions keeping AI Champions current on new AI tools, frameworks and agentic workflow practices; prompt libraries, skills, workflows and reusable components actively used across multiple teams and use cases; solutions integrating effectively with approved enterprise data, hosting, identity, API and AI Factory services; testing identifying functional, security, model-quality and guardrail issues before solutions are released to users; technical defects, integration requests and deployment dependencies identified early and resolved or escalated promptly; code, configurations, interfaces and deployment procedures documented clearly enough to support