Job Description

The Enterprise AI Architect is responsible for defining and governing the enterprise AI platform architecture, ensuring secure, scalable, compliant, and cost-effective AI solutions across the organization. This role provides strategic technical leadership for AI capabilities spanning data integration, retrieval-augmented generation (RAG), model orchestration, agentic AI, observability, FinOps, and security. Serving as the organization's AI architecture authority, the position establishes enterprise standards, reviews vendor solutions, and drives alignment across Commercial, R&D, Regulatory, TechOps, Data, and Information Security teams. The role partners with senior business and technology leaders to enable AI innovation, ensure responsible AI adoption, and develop a sustainable enterprise AI ecosystem that supports long-term business objectives.

• AI platform architecture — end-to-end ownership of AIP architecture across the integration layer, RAG pipelines, orchestration, and model layer; ensure consistency across all four layers and vendor-built solutions

• Data-to-AI contract and integration layer design — define how Gold datasets are exposed to AI, chunking/embedding/indexing standards, and RBAC propagation; own ingestion pipeline standards and drive the Phase 1 to Phase 2 transition

• FinOps and observability — own model cost tracking, token usage monitoring, and AI observability tooling; establish baseline measurement and ongoing cost governance

• Vendor technical submission review — review every vendor AI solution design for RBAC implementation, prompt injection exposure, data leakage controls, audit trail completeness, and platform standards compliance

• Model gateway design and agent development toolkit — define model routing/fallback architecture; establish standards and governance for agentic AI development

• AI security review collaboration — partner with Global InfoSec to define and execute the technical AI security review process

• Governance, standards, and business alignment — define metadata standards, AI design patterns, and model usage policies; partner with Commercial, R&D, Regulatory, and TechOps

• Guide third-party implementation vendors on architecture decisions and integration patterns; prevent vendor lock-in; ensure effective knowledge transfer so Amneal retains architectural ownership

• Define citizen development standards in partnership with the Enterprise Data Architect

• Work with AI Solutions Engineers embedded in business functions