Architect Jobs

About The Position

Requirements

• Bachelors Degree (BA/BS) Computer Science, Data Engineering, Information Systems, or related field — or equivalent work experience
• 12 years or more in IT/enterprise architecture experience, including enterprise-level AI platform architecture (not project/solution level)
• Four-layer enterprise AI architecture (data platform, integration, orchestration, model layer)
• Model gateway / routing / fallback design
• RAG pipeline design at production scale
• AI governance frameworks and Architecture Review Board (ARB) standards
• FinOps for AI workloads - Model cost monitoring, token usage tracking, cost allocation across a multi-use-case AI portfolio, built from scratch
• Data-to-AI integration design - RBAC propagation, metadata standards, data contracts from governed data to AI systems
• Multi-LLM provider architecture - OpenAI, Anthropic, AWS Bedrock, or equivalent; vendor-agnostic by design
• AWS data/AI ecosystem - S3, Glue, SageMaker, Bedrock, or equivalent
• Databricks/lakehouse platform - Databricks or similar
• Regulated industry deployment - Pharma, financial services, or healthcare; audit trail, access control, compliance constraints in practice
• Vendor governance - Directing external vendors on architecture decisions and knowledge transfer
• 21 CFR Part 11 / GxP AI deployment
• Agentic AI architecture - Multi-agent orchestration, memory management, tool use, human-in-the-loop design
• AI security - IAM for AI workloads, DLP for LLM I/O, prompt controls, audit logging, AI threat modeling
• Citizen development governance - Guardrails for non-technical users building AI workflows (M365 Copilot Studio, Claude Projects, or equivalent)
• Agent observability tooling - Braintrust, Lang Smith, or equivalent
• AI FinOps tooling - Model cost dashboards and chargeback frameworks

Nice To Haves

• Master Degree (MS/MA) Computer Science, Data Science, or related field

Responsibilities

• 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

Benefits

• short-term incentive opportunity, such as a bonus or performance-based award
• comprehensive, flexible and competitive benefits program
• above-market, diverse and robust health and insurance benefits
• significant 401(k) matching contribution
• employee well-being programs