Head of Enterprise AI

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 & gov...