Senior Manager AI Architecture Innovation

Hexion Careers

About The Position

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

Requirements

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
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.

Nice To Haves

Familiarity with AI security patterns: prompt injection defense, access controls, data governance, compliance-by-design, and cost governance for model routing and inference optimization.
Specialty chemicals, industrial manufacturing, or process industry experience a plus — familiarity with IoT/sensor data, SAP AI extensions, and domain-specific AI use cases.

Responsibilities

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

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