About the role
Embedded inside enterprise delivery teams, you work closely with global, cross-regional teams to turn prioritised GenAI use cases into production software - building, integrating, and hardening Azure-based AI solutions and accelerating adoption within the teams you join. You deliver production software and help the teams you join work faster. As an embedded AI Implementation Engineer, you help convert prioritised use cases into shipped, governed, measurable software.
Responsibilities:
- Build and Ship: Implement GenAI features end-to-end on Azure, including RAG pipelines, AI agents, APIs, and UI integrations, while connecting with enterprise systems and data.
- Embed and Enable: Work closely within delivery pods, collaborate and pair with engineers, remove technical blockers, and transfer GenAI knowledge to ensure sustainable adoption.
- Productionize: Build evaluation frameworks, observability, guardrails, caching, and CI/CD capabilities to transform prototypes into reliable, scalable, secure, and cost-efficient production services.
- Integrate Securely: Connect AI solutions securely with enterprise data and systems, ensuring appropriate access controls, secrets management, compliance, and secure handling of sensitive data.
- Iterate on Quality: Continuously improve solution quality using evaluation results and user feedback, focusing on grounding, accuracy, latency, reliability, and cost optimisation.
- Measure and Align: Track delivery, adoption, quality, performance, and business-impact metrics, ensuring alignment with overall program objectives and targets.