AI Forward Deployed Engineer

Job Description

Job Role

AI Forward Deployed Engineer

Grade

Senior Manager

Reporting to

AI Project Portfolio Lead

Location

Dubai, United Arab Emirates

Job Purpose

About Network International: Network International is the largest financial technology company in the Middle East and Africa. Payments is our core business, and we provide services across more than 50 countries, including key markets such as the UAE, Jordan, South Africa and Egypt. In addition to payments, we offer solutions across data and insights, lending, insurance and risk management. Our customers include businesses of every scale and segment, with growing direct-to-consumer card offerings.

Our Employee Value Proposition: At Network International, we always stay ahead. In the fast-paced world of financial services, we thrive on innovation, agility and purposeful collaboration. We invest first in our people, empowering them to make bold decisions, learn fast and develop their expertise alongside industry leaders. Here, solving complex problems means more than using cutting-edge technology; it is about creating meaningful value for our customers together. We foster a culture where trust, accountability and achievement go hand in hand, because success is not just a goal; it is how we work every day, as one team.

About the Role

The AI Forward Deployed Engineer embeds directly into priority enterprise AI projects — with a 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, making sure solutions are secure, scalable and reusable rather than one-off demos.

Working under the direction of the AI Project Portfolio Lead, the role collaborates with AI Champions, Data Scientists, Technology, Architecture, Data and Information Security teams to deliver enterprise-grade AI solutions at pace. While solution scoping and prioritisation sit with the AI Portfolio & Solutioning Lead, this role validates and enhances designs at the point of implementation, feeding practical build learnings back into solutioning, the AI Factory and shared engineering standards. It also enables AI Champions to build solutions independently by making the right architecture choices, providing reusable components and offering practical, hands-on technical support across development, testing and deployment.

Key Accountabilities

• Embed into selected AI Champion and business-unit projects to implement approved solution designs as working technical components, applications, agents, automations and integration flows.
• 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.
• 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.
• 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.
• Build and maintain reusable prompt templates, skills, tools, workflows, code components and reference implementations for organisation-wide use.
• Integrate AI solutions with approved enterprise data sources, APIs, applications, identity services, hosting environments and AI Factory capabilities.
• 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.
• Prepare and execute functional testing, integration testing, user acceptance testing, model evaluation, red-teaming and guardrail testing for selected solutions.
• Support AI Champions and project teams in defining test scenarios, acceptance criteria, evaluation datasets and quality thresholds.
• Apply approved security, privacy, responsible AI and engineering controls throughout development, testing and deployment.
• Diagnose and resolve technical issues involving prompts, agents, APIs, integrations, authentication, data access, environments and platform configurations.
• 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.
• Monitor early production performance and support the resolution of technical issues following deployment.
• Identify opportunities to improve solution quality, engineering standards, developer experience, reuse and delivery speed across the AI transformation program.
• Provide clear technical updates, risks and recommendations to the AI Portfolio & Solutioning Lead and relevant project stakeholders.
• Contribute to a collaborative, accountable and performance-driven engineering culture focused on secure delivery and measurable business value.

Experience, Skills & Success Profile

Experience & Qualifications

• 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.
• Sound 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.
• 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

• Demonstrates strong hands-on engineering, coding, configuration and technical problem-solving capability.
• Converts approved solution designs into clear, maintainable and testable technical implementations, making pragmatic architecture decisions along the way.
• Selects appropriate AI, integration and automation patterns based on business requirements, technical feasibility and operational constraints.
• Understands the strengths and limitations of large language models, agents, retrieval systems, workflow automation and traditional software components.
• Writes clear, secure and maintainable code and applies appropriate development, testing and documentation standards.
• Diagnoses technical issues methodically and drives problems through to practical resolution.
• Maintains strong attention to detail across code, configurations, data flows, access controls, testing and release activities.
• Applies security, privacy, responsible AI and regulatory requirements throughout the engineering lifecycle.
• Communicates complex technical concepts clearly to business users, AI Champions and nontechnical stakeholders.
• Provides practical coaching and constructive technical guidance without unnecessarily taking ownership away from AI Champions.
• Collaborates effectively as a peer with the AI Portfolio & Solutioning Lead, data scientists, architects, platform teams, developers and Information Security stakeholders.
• Balances rapid prototyping with the engineering discipline required for scalable and production-ready solutions.
• Plans and prioritises effectively across multiple use cases, technical requests and delivery dependencies.
• Identifies reusable components and common technical patterns that reduce duplication and accelerate future delivery.
• Takes clear ownership of technical deliverables, defects and dependencies and communicates risks early.
• Learns new models, platforms, frameworks and development practices quickly and shares that knowledge through practical enablement of AI Champions.

What Success Looks Like in This Role

• Approved AI solution designs are implemented as working technical components and applications with clear traceability to business requirements.
• Sound architecture and integration decisions ensure solutions are secure, scalable, maintainable and reusable, not just functional demos.
• Selected AI applications, agents and automations are delivered to agreed quality, security, performance and timeline expectations.
• Prototypes are developed quickly without compromising the controls required for pilot and production deployment.
• AI Champions receive practical, hands-on technical support and become increasingly capable of building high-quality solutions independently.
• Internal enablement sessions keep AI Champions current on new AI tools, frameworks and agentic workflow practices.
• Prompt libraries, skills, workflows and reusable components are actively used across multiple teams and use cases.
• Solutions integrate effectively with approved enterprise data, hosting, identity, API and AI Factory services.
• Testing identifies functional, security, model-quality and guardrail issues before solutions are released to users.
• Technical defects, integration requests and deployment dependencies are identified early and resolved or escalated promptly.
• Code, configurations, interfaces and deployment procedures are documented clearly enough to support review, maintenance and operational handover.
• Production releases are controlled, stable and supported by effective coordination with Technology and operational teams.
• AI solutions demonstrate reliable performance, appropriate guardrails and measurable business value.
• Engineering standards, reusable solution patterns and delivery efficiency improve across the AI transformation program.
• Stakeholders view the role as technically credible, responsive, practical and focused on delivering working solutions.
• The role contributes positively to responsible innovation, technical quality, collaboration and continuous improvement.

End-note disclaimer

Network International is committed to providing fair and equal opportunities for all candidates and employees. We value inclusion, diversity and equity, and are committed to creating a workplace where everyone is respected, supported and able to contribute to their full potential. All employment decisions are based on merit, business needs and role requirements, without discrimination.