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 procurementlead 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 commercials 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 & optimizationdefine 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

Basic Qualifications

• Bachelors (12 yrs) Masters (10 yrs) relevant experience.
• Proven success deploying GenAI in commercial or enterprise settings and scaling from pilot to production.
• Hands-on experience building AI agents/RAG with strong Python; practical LLM ops (prompting evals guardrails cost/perf tuning).
• Deep enterprise integration experience (REST webhooks eventing) and connecting AI to core ERP/CRM/MES/DWH/SaaS platforms.
• Excellent communicator who simplifies complexity and navigates cross-functional stakeholders (execs frontline).
• Strong analytical mindsetdefines success metrics measures outcomes and iterates.

Preferred Qualifications

• Governance experience: policy frameworks DPIA/PIA export controls data residency (incl. China) model risk.
• Manufacturing/semiconductor background; familiarity with Oracle EBS Salesforce Opcenter/Camstar ServiceNow Snowflake/Databricks.
• Experience evaluating third-party AI (e.g. Microsoft Copilot/Azure OpenAI Glean/Moveworks AWS Bedrock) and negotiating vendor SOWs.
• Change-management frameworks; ability to craft AI literacy and enablement programs.

Working Conditions

• Hybrid position (three days a week in office)
• Working conditions are those typically found in an office corporate environment.

Physical Requirements

• Physical requirements are those typically associated with a professional role - sitting computer and accessories use.

Safety Requirements

All employees are required to follow the site EHS procedures and Coherent Corp. Corporate EHS standards.

Quality and Environmental Responsibilities

Depending on location this position may be responsible for the execution and maintenance of the ISO 9000 and/or other applicable standards that may apply to the relevant roles and responsibilities within the Quality Management System and Environmental Management System.

CultureCommitment

Ensure adherence to companys values (ICARE) in all aspects of your position at Coherent Corp.:

Integrity Create an Environment of Trust

Collaboration Innovate Through the Sharing of Ideas

Accountability Own the Process and the Outcome

Respect Recognize the Value in Everyone

Enthusiasm Find a Sense of Purpose in Work

Coherent Corp. is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex gender identity sexual orientation race color religion national origin disability protected Veteran status age or any other characteristic protected by law.