Primary Duties & Responsibilities GenAI Strategy & Solution Development Lead the enterprise GenAI strategy and multi-year roadmap; bring sustainable methodologies (evals, safety, cost/perf, lifecycle). Design, prototype, and ship AI agents/RAG/search, document automation, knowledge assistants, and workflow copilots tied to measurable outcomes. Pressure-test external solutions for explainability, sustainability, and model-evolution roadmaps; recommend build vs. buy. Partner with IT on platform choice and reference architectures (vector DB, policy/guardrails, observability, prompt/eval stores); guide design for internally built solutions. Assist business owners with AI procurement-lead technical due diligence, security/compliance feasibility, and integration planning with commercial and IT. Technical and Operational Responsibilities AI Solution Development: Oversee the architecture, design, and deployment of AI/ML solutions across the enterprise with emphasis on: Deep learning (CNNs, RNNs, transformers, attention-based architectures) Generative AI and LLMs (OpenAI, Anthropic, Azure/OpenAI Service, Hugging Face) Predictive and prescriptive analytics (time series forecasting, anomaly detection, optimization) Computer vision and NLP for enterprise use cases (quality inspection, document intelligence, conversational AI) Generative AI Integration: Drive enterprise-grade integration of GenAI into business workflows by connecting LLMs to internal knowledge repositories and systems (RAG, agent frameworks, secure APIs). MLOps & Scalability: Build scalable AI infrastructure and pipelines with CI/CD for ML models. Implement monitoring, drift detection, retraining, and explainability frameworks. Leverage cloud AI/ML platforms (Azure ML, AWS Sagemaker, GCP Vertex AI) for enterprise deployments. AI-Enabled Operations: Partner with R&D, supply chain, manufacturing, customer operations, and IT to embed AI into core business systems and products. Data & Infrastructure: Ensure availability of high-quality, governed data pipelines (ETL/ELT, feature stores, vector databases). Familiarity with modern data stack tools (Databricks, Snowflake, Spark, Kafka). Strong grounding in data security, privacy, and compliance requirements (GDPR, CCPA, ITAR, CMMC, AI ethics frameworks). Governance & Risk Chair the AI Governance Council; define decision rights, guardrails, and approval workflows. Establish model/tool approval, DPIA/PIA, data classification/retention, export-control checks, human-in-the-loop, and audit logging. Maintain the AI/LLM registry, model cards, usage monitoring, red-team testing, and incident playbooks. Cross-Functional AI Initiatives Work with Business Groups to plan cross-functional use cases; act as commercial's POC for AI policy & governance. Bring industry best practices in DS/AI for commercial/manufacturing use cases; collaborate with business groups on training, tools, and reusable patterns. Serve as secondary POC for measurement & optimization-define adoption/ROI metrics, experimentation plans, and continuous improvement. Integration & Delivery Integrate AI with enterprise systems via REST APIs, events, ETL/ELT (Oracle EBS, Salesforce, MES/Opcenter, DWH/Snowflake/Databricks, M365). Stand up shared platform capabilities: guardrails/safety, evals, observability, cost governance, secrets/keys, identity (SSO/OIDC). Run pilot → scale playbooks; publish templates (prompts, patterns, SDKs) and manage change with clear success criteria. Education, Experience