AI Forward Deployed Engineer

About the role

The AI Forward Deployed Engineer embeds directly into priority enterprise AI projects, turning approved solution designs into production‑ready applications, agents, automations and integration flows. Reporting to the AI Project Portfolio Lead, the role works closely with AI Champions, Data Scientists and technology teams to deliver secure, scalable, reusable AI solutions.

Key responsibilities

• Implement approved AI solution designs as technical components, applications, agents, automations and integration flows.
• Make and enforce sound technical, integration and architecture decisions to ensure security, scalability and maintainability.
• Design, prototype, build, configure and test AI applications, copilots, agents and orchestration workflows using enterprise platforms.
• Coach AI Champions in building data pipelines, agentic workflows and software applications while reinforcing standards.
• Stay ahead of emerging AI technologies, create reusable prompt templates, skills, tools and reference implementations.
• Integrate AI solutions with enterprise data sources, APIs, identity services and hosting environments.
• Prepare and execute functional, integration, user acceptance, model evaluation and guardrail testing.
• Maintain technical documentation, track defects and coordinate code review, release management and production handover.

Required profile

• Extensive hands‑on experience in software or AI engineering, preferably as an embedded engineer.
• Bachelor’s degree in computer science, software engineering, data science or equivalent experience.
• Proven experience designing, developing, testing and deploying enterprise AI‑enabled solutions.
• Working knowledge of machine learning tooling and model evaluation; model‑building experience is a plus.

Required skills

• Strong programming in Python, JavaScript, TypeScript or C#.
• Experience with APIs, SDKs, web services, authentication mechanisms and enterprise integrations.
• Familiarity with cloud AI services, enterprise AI platforms, low‑code development environments and automation tools.
• Version control, code review, CI/CD practices and testing frameworks.
• Understanding of software architecture, application security, identity & access management and data privacy.