Director, Enterprise AI Architect
Amneal Pharmaceuticals
August 11, 2026
Remote friendly (Bridgewater, NJ)
United States
IT
Enterprise AI Architect
Responsibilities
- Define and govern enterprise AI platform architecture across the integration layer, RAG pipelines, orchestration, and model layer; ensure consistency across layers and vendor solutions.
- Design data-to-AI contract/integration layer: Gold dataset exposure, chunking/embedding/indexing standards, RBAC propagation; own ingestion standards and drive Phase 1 to Phase 2 transition.
- Own FinOps and observability: model cost tracking, token usage monitoring, AI observability tooling; establish baseline measurement and ongoing cost governance.
- Review vendor AI technical submissions for RBAC implementation, prompt-injection exposure, data leakage controls, audit trail completeness, and platform standards compliance.
- Design model gateway (routing/fallback) and establish standards/governance for agentic AI development.
- Partner with Global InfoSec to define and execute the technical AI security review process.
- Define metadata standards, AI design patterns, and model usage policies; align with Commercial, R&D, Regulatory, TechOps, Data, and Information Security.
- Guide third-party vendors on architecture/integration patterns to prevent lock-in and ensure knowledge transfer.
- Define citizen development standards with the Enterprise Data Architect.
- Work with AI Solutions Engineers embedded in business functions.
Qualifications
- Required: Bachelor’s degree in CS/Data Engineering/Information Systems or related field, or equivalent experience.
- Preferred: Master’s degree in CS/Data Science or related field.
- 12+ years IT/enterprise architecture experience, including enterprise-level AI platform architecture.
Specialized Knowledge/Skills (required/preferred as stated)
- Four-layer enterprise AI architecture; model gateway/routing/fallback; production-scale RAG.
- AI governance/Architecture Review Board standards; FinOps for AI workloads.
- Data-to-AI integration (RBAC, metadata standards, data contracts); multi-LLM provider, vendor-agnostic architecture.
- AWS ecosystem and/or Databricks/lakehouse platforms.
- Regulated industry deployment experience; 21 CFR Part 11 / GxP AI deployment.
- Agentic AI architecture (multi-agent orchestration, memory, tool use, human-in-the-loop).
- AI security (IAM, DLP for LLM I/O, prompt controls, audit logging, threat modeling).
- Citizen development governance; agent observability tooling; AI FinOps tooling.
Compensation/Benefits
- Base salary: $240,000–$270,000/year plus potential short-term incentive within first 12 months; benefits program includes health/insurance and 401(k) matching.
Responsibilities
- Define and govern enterprise AI platform architecture across the integration layer, RAG pipelines, orchestration, and model layer; ensure consistency across layers and vendor solutions.
- Design data-to-AI contract/integration layer: Gold dataset exposure, chunking/embedding/indexing standards, RBAC propagation; own ingestion standards and drive Phase 1 to Phase 2 transition.
- Own FinOps and observability: model cost tracking, token usage monitoring, AI observability tooling; establish baseline measurement and ongoing cost governance.
- Review vendor AI technical submissions for RBAC implementation, prompt-injection exposure, data leakage controls, audit trail completeness, and platform standards compliance.
- Design model gateway (routing/fallback) and establish standards/governance for agentic AI development.
- Partner with Global InfoSec to define and execute the technical AI security review process.
- Define metadata standards, AI design patterns, and model usage policies; align with Commercial, R&D, Regulatory, TechOps, Data, and Information Security.
- Guide third-party vendors on architecture/integration patterns to prevent lock-in and ensure knowledge transfer.
- Define citizen development standards with the Enterprise Data Architect.
- Work with AI Solutions Engineers embedded in business functions.
Qualifications
- Required: Bachelor’s degree in CS/Data Engineering/Information Systems or related field, or equivalent experience.
- Preferred: Master’s degree in CS/Data Science or related field.
- 12+ years IT/enterprise architecture experience, including enterprise-level AI platform architecture.
Specialized Knowledge/Skills (required/preferred as stated)
- Four-layer enterprise AI architecture; model gateway/routing/fallback; production-scale RAG.
- AI governance/Architecture Review Board standards; FinOps for AI workloads.
- Data-to-AI integration (RBAC, metadata standards, data contracts); multi-LLM provider, vendor-agnostic architecture.
- AWS ecosystem and/or Databricks/lakehouse platforms.
- Regulated industry deployment experience; 21 CFR Part 11 / GxP AI deployment.
- Agentic AI architecture (multi-agent orchestration, memory, tool use, human-in-the-loop).
- AI security (IAM, DLP for LLM I/O, prompt controls, audit logging, threat modeling).
- Citizen development governance; agent observability tooling; AI FinOps tooling.
Compensation/Benefits
- Base salary: $240,000–$270,000/year plus potential short-term incentive within first 12 months; benefits program includes health/insurance and 401(k) matching.