AI Forward Deployed Engineer

About the Role
The AI Forward Deployed Engineer embeds directly into priority enterprise AI projects—particularly those led by AI Champions—to transform approved solution designs into working, production-ready applications, agents, automations, and integration flows. This 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 that 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, and Data and Information Security teams to deliver enterprise-grade AI solutions swiftly. While solution scoping and prioritization are handled by the AI Portfolio & Solutioning Lead, this role enhances designs at the implementation stage, feeding practical build insights back into solutioning, the AI Factory, and engineering standards. It also empowers AI Champions to build solutions independently by making the right architectural choices, providing reusable components, and offering hands-on technical support during 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 ensuring solutions are secure, scalable, maintainable, and reusable across the organization.
• Validate, pressure-test, and enhance solution designs at implementation, providing practical build insights to the AI Portfolio & Solutioning Lead.
• Design, prototype, build, configure, and test AI applications, copilots, agents, automations, and orchestration workflows using approved 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 in designing and building data pipelines, agentic workflows, automations, and software applications while reinforcing approved technical and delivery standards.
• Stay updated on emerging AI technologies, tools, and frameworks for building applications and agentic workflows, facilitating internal sessions to teach and upskill AI Champions.
• Build and maintain reusable prompt templates, skills, tools, workflows, code components, and reference implementations for organizational 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 AI engineering patterns based on solution requirements.
• Apply machine learning knowledge to operate AI tooling, run Python, and evaluate model outputs; hands-on model building experience is appreciated but not essential.
• 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, preferably 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 in 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 ability 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 during 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 non-technical or citizen developers in building controlled applications and automations is advantageous.
• Experience in 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 capabilities.
• Converts approved solution designs into clear, maintainable, and testable technical implementations, making pragmatic architecture decisions throughout the process.
• 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 while applying appropriate development, testing, and documentation standards.
• Diagnoses technical issues methodically and drives them 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 non-technical stakeholders.
• Provides practical coaching and constructive technical guidance without unnecessarily taking ownership away from AI Champions.
• Collaborates effectively with peers, including 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 prioritizes 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 ownership of technical deliverables, defects, and dependencies while communicating risks early.
• Learns new models, platforms, frameworks, and development practices quickly, sharing 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, rather than merely 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 independently building high-quality solutions.
• 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.

*The company 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.