Senior Manager AI Architecture Innovation

Define and own the enterprise GenAI platform strategy that makes every AI use case at Hexion possible. Build and operate reusable GenAI platform services - grounding, orchestration, memory, evaluation, and LLMOps - as production-grade shared infrastructure that accelerates delivery across Commercial, Supply Chain, R&D, and Manufacturing.

Reports to Director, AI Architecture

This role is the engineering foundation of Hexion's AI transformation. The Senior Manager will build the GenAI platform layer that accelerates every use case across the enterprise - making safe, reliable, production-grade AI the default, not the exception

Define and execute the enterprise GenAI technology strategy and platform roadmap, aligning capabilities to business priorities and measurable outcomes across all domains.

- Lead delivery of core GenAI platform services - retrieval/grounding, knowledge foundations, memory patterns, and agent orchestration - as reusable capabilities for teams across the enterprise.

- Partner with Data & Analytics to ensure AI-ready data foundations: governed access, data quality, lineage/metadata, indexing pipelines, and standardized measurement and telemetry.

- Establish LLMOps and AI quality standards: evaluation frameworks, release gates, drift monitoring, and lifecycle management for prompts and models to ensure consistent performance at scale.

- Embed security, privacy, Responsible AI, and compliance-by-design across all solutions through guardrails, risk reviews, auditability, and red-teaming practices.

- Own operational excellence and cost governance: reliability/SLOs, incident readiness, performance optimization, model routing/caching, and dashboards that manage latency and spend.

- Run AI Innovation portfolio, guide architecture and design for AI POVs, act as SME on make vs buy vs partner

- Platform architect mindset - builds shared, reusable GenAI infrastructure that makes every product team faster rather than solving one-off problems.

- Engineering-first and delivery-oriented - pragmatic about what ships, not just what's architecturally elegant; known for enabling teams rather than gatekeeping them.

- Security and quality conscious - treats responsible AI, compliance-by-design, and operational reliability as core platform features, not afterthoughts.

- Collaborative and credible - earns trust from product teams, data scientists, security, and business leaders through technical depth and clear communication

- 10+ years of progressive software architecture and 3-5 years of AI/ML engineering experience; at least 3 years directly owning GenAI platform strategy, LLMOps, or enterprise AI infrastructure delivery.

- Demonstrated track record shipping GenAI or distributed AI systems at enterprise scale - from architecture and platform design through LLMOps, monitoring, and continuous improvement - with measurable reliability and performance outcomes.

- Experience building and leading engineering teams in transformation-stage enterprises; ideally with exposure to specialty chemicals, industrial manufacturing, or process industries and their AI/data integration patterns

- Familiarity with AI security patterns: prompt injection defense, access controls, data governance, compliance-by-design, and cost governance for model routing and inference optimization. (Preferred)

- Specialty chemicals, industrial manufacturing, or process industry experience a plus - familiarity with IoT/sensor data, SAP AI extensions, and domain-specific AI use cases. (Preferred)

-10+ years in software architecture, platform engineering, or 3-5 years AI/ML engineering; 3+ years leading GenAI or enterprise AI platform delivery at scale.

- Deep expertise in the Microsoft/Azure ecosystem: Azure OpenAI, AI Foundry, Cognitive Services, Semantic Kernel, and enterprise-scale cloud architecture patterns.

- Strong command of the full AI stack: experience surfaces, agentic orchestration (LangGraph, AutoGen, Semantic Kernel), knowledge/memory systems, model runtimes, evaluation frameworks, and infrastructure.

- Proven track record shipping AI-driven or distributed software systems at enterprise scale, with measurable reliability, performance, and cost outcomes.

- Strong understanding of hybrid AI architectures: local and cloud inference, model orchestration, multimodal runtimes, and RAG pipeline design.

- Expertise in LLMOps: evaluation frameworks, release gates, prompt/model lifecycle management, drift monitoring, and responsible AI guardrails including red-teaming and auditability.

- Hands-on proficiency with Azure AI Foundry, Azure OpenAI Service, Copilot Studio, and Microsoft Fabric; familiar with AKS/Kubernetes and CI/CD pipelines for AI workloads.

We are an Equal Opportunity, Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to gender, pregnancy, race, national origin, religion, age, sexual orientation, gender identity, veteran or military status, status as a qualified individual with a disability or any other characteristic protected by law.

To be considered for this position candidates are required to submit an application for employment through our career site and, be at least 18 years of age. Any offer of employment will be conditioned upon successful completion of a drug test and background investigation, as well as authorization for the Company to conduct additional periodic background checks as required by the Chemical Facility Anti-Terrorism Standards (CFATS) or regulations adopted by the department of Homeland Security or other regulatory agencies.

A prior criminal record is not an automatic bar to employment, and the Company will conduct an individualized assessment and reassessment, consistent with applicable law, prior to making any final employment decision.