Director, AI Architecture & Engineering - US Remote

Hexion Inc. is a company that pushes boundaries and creates impactful solutions through science. They are seeking a Director of AI Architecture & Engineering to own the full-stack AI engineering and architecture foundation that powers their CaaS transformation, focusing on designing and operating an enterprise AI platform across various domains.

Responsibilities:

• Define and own Hexion’s enterprise AI architecture: multi-agent frameworks, LLM/GenAI platforms, ML pipelines, data layer, and integration patterns across SAP S/4HANA, Salesforce, OSIsoft PI, and cloud environments (Azure/AWS)
• Build and scale the agentic AI platform: multi-agent orchestration (LangGraph/CrewAI), RAG pipelines, tool-calling frameworks, guardrails, and human-in-the-loop controls for autonomous workflows across business functions
• Establish and operate enterprise MLOps: model registry, CI/CD for AI, model versioning, drift monitoring, inference optimization, and closed-loop retraining pipelines — ensuring all models are production-grade and auditable
• Architect the data foundation for AI: feature stores, semantic/vector layers, real-time streaming (Kafka/event-driven), data quality pipelines, and synthetic data generation for specialty chemicals use cases
• Lead AI security and responsible AI engineering: prompt injection defense, model explainability (XAI), bias detection, access controls, and compliance with enterprise data policy and regulatory requirements (REACH, GHS, SOX)
• Serve as the technical authority connecting AI CoE, IT, domain teams (R&D, commercial, manufacturing), and external vendors — translating business requirements into scalable AI system designs
• Evaluate and manage AI technology vendors, cloud providers, and open-source frameworks; make strategic buy/build/partner decisions for the AI stack with clear cost, capability, and risk analysis
• Build and lead the AI engineering team: ML engineers, data engineers, LLM engineers, MLOps specialists, and AI platform engineers — hiring, mentoring, and creating a culture of engineering excellence
• Champion engineering standards: modular/reusable AI solution patterns, API-first design, observability, SRE practices for AI systems, and documentation to accelerate cross-functional deployment
• Drive Hexion’s Perception Intelligence, Predictive Modeling, and Adaptive Closed-Loop Control capabilities — including sensor fusion from OSIsoft PI, digital twin integration, and process optimization AI for manufacturing
• Own the AI infrastructure roadmap — balancing rapid experimentation and disciplined production delivery — and report engineering KPIs, platform reliability, and build-vs-buy decisions to VP/Director, AI Transformation (Sambit)

Requirements:

• 10+ years in AI/ML engineering, data engineering, or enterprise architecture; 3+ years in a senior technical leadership role owning an AI platform or engineering team
• Deep expertise in GenAI/LLM systems: RAG, fine-tuning, prompt engineering, agentic frameworks (LangGraph, CrewAI, AutoGen), and multi-agent orchestration at enterprise scale
• MLOps mastery: model lifecycle management, CI/CD for ML, drift monitoring, feature stores, model registries (MLflow, Kubeflow, SageMaker, Azure ML), and inference optimization
• Cloud-native AI infrastructure: Azure/AWS AI services, containerized ML workloads (Docker/Kubernetes), event-driven architecture (Kafka), APIs, IaC, and observability/SRE practices
• Data architecture for AI: feature engineering, vector/semantic layers, real-time streaming, data quality pipelines, and integration with industrial systems (OSIsoft PI, SAP S/4HANA, Salesforce or Dynamics 365)
• Responsible AI engineering: XAI/explainability, bias detection, prompt injection defense, AI security, and compliance frameworks relevant to specialty chemicals (REACH, GHS, data sovereignty)
• High level of proficiency with ERP and advanced planning systems (SAP IBP, S/4HANA, APO, o9, Kinaxis)
• Python/SQL fluency; working knowledge of ML frameworks (PyTorch, scikit-learn, HuggingFace); ability to review and contribute to production AI code
• Prior experience building AI teams from the ground up in transformation-stage or industrial enterprises; track record of hiring and growing ML engineers and data engineers