AI Specialist

The AI Specialist designs, develops and operationalizes secure, scalable, high-value AI, machine learning and generative AI solutions for HOYA divisions and corporate functions. The role leads hands-on delivery throughout the AI lifecycle, covering data preparation, experimentation, model and agent development, evaluation, responsible AI validation, deployment, integration, monitoring and optimization. Working with division technology teams, group architects, data engineers, security teams and other stakeholders, the AI Specialist translates enterprise architecture, security and governance requirements into production-ready cloud solutions and develops reusable engineering patterns and accelerators to support safe, secure AI delivery by each division.

Job Responsibilities

• Work with divisions and group stakeholders to identify common AI needs across use cases, run joint proofs of concept with division teams where this accelerates learning, and shape the results into candidate accelerators, patterns and standards with clear, measurable value.
• Build and maintain the common AI layer and its reusable machine-learning, deep-learning, generative AI and agentic AI components and reference implementations, on Azure AI Foundry, Azure Machine Learning, Azure AI Search, M365 Copilot Studio and related Azure services.
• Build reference agents, orchestration flows, tools and custom connectors, including Model Context Protocol (MCP) integrations. Codify agent memory, tool-use, human-in-the-loop and workflow patterns so that divisions can apply them consistently.
• Develop reusable experimentation and evaluation frameworks, covering model comparison, RAG evaluation, hallucination and groundedness testing and safety testing, which divisions can run against their own solutions.
• Define and build reference feature-store patterns for machine-learning solutions, covering feature engineering, versioning, validation, discovery, online and offline serving, monitoring and governance.
• Write production-grade code, APIs, containers and components for the accelerators themselves. Set the engineering standards that go with them, including source control, automated testing, CI/CD, infrastructure as code and secure development practices.
• Design deployment and integration blueprints that show divisions how to embed AI solutions into their applications, processes and channels, with identity, access control, secrets management, network security, content safety, data protection and audit requirements built in.
• Build template MLOps and LLMOps pipelines, with release management, reproducibility, approval workflows, rollback and controlled promotion across environments, which divisions can adopt with minimal rework.
• Define monitoring and optimization patterns for quality, latency, availability, cost, drift and security, and advise division teams when they investigate incidents or tune their solutions.
• Package proven approaches into well-documented accelerators, with sample code, configuration and adoption guidance, so that division IT teams can adapt them quickly to specific use cases.
• Work with architects, security, risk, data governance, infrastructure and division teams on architecture and risk reviews, and help divisions scale AI solutions responsibly on top of the group assets.

Qualifications

• Bachelor's degree in computer science, artificial intelligence, machine learning, data science, or a related quantitative discipline.
• At least 6 years of hands-on experience building and productionizing machine-learning solutions, with recent work in generative AI and agentic AI.
• Microsoft certifications such as Azure AI Apps and Agents Developer Associate, Machine Learning Operations Engineer Associate or Multi-Agent AI Solutions Expert are preferred.
• Strong written and spoken English, and the ability to work well in a cross-functional, multicultural and geographically distributed organization.

Experience

• Extensive hands-on expertise with Azure AI Foundry, Azure Machine Learning and Azure AI Search.
• Strong MLOps/LLMOps expertise, covering model and prompt versioning, experiment tracking, evaluation pipelines, model registries, deployment strategies, monitoring, drift detection and rollback, preferably on the Azure stack.
• Strong Python and software engineering skills, including REST APIs, Git, automated testing, containers and CI/CD.
• Practical experience with model fine-tuning, evaluation, deployment, monitoring and optimization.
• Hands-on experience with RAG, embeddings, vector databases, prompt engineering, agentic architecture, tool integration and orchestration.
• Experience designing and delivering scalable, reliable and cost-effective solutions on Azure, including performance and operational troubleshooting.
• Experience with manufacturing AI use cases, such as automated defect detection and classification, yield prediction and automated diagnostics, would be an advantage.
• Strong understanding of AI security, responsible AI, privacy, access control, content safety, threat modelling and secure handling of enterprise data.
• Experience with Microsoft Fabric, Azure Functions, API Management, Logic Apps, Kubernetes or related integration and runtime services is preferred.
• Experience with multi-agent solutions, Agent Security Posture Management, AI observability and production GenAI evaluation frameworks is preferred.