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About This Role
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.
Essential Functions:* 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
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Additional Responsibilities:* 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
Education:
- Bachelors Degree (BA/BS) Computer Science, Data Engineering, Information Systems, or related field — or equivalent work experience \- Required
- Master Degree (MS/MA) Computer Science, Data Science, or related field \- Preferred
Experience:
- 12 years or more in IT/enterprise architecture experience, including enterprise\-level AI platform architecture (not project/solution level)
Specialized Knowledge:
- 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
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The base salary for this position ranges from $240,000 to $270,000 per year. In addition, Amneal offers a short\-term incentive opportunity, such as a bonus or performance\-based award, with this position within the first 12 months. Amneal ranges reflect the Company’s good faith estimate of what Amneal reasonably believes that it will pay for said position at the time of the posting. Individual compensation will ultimately be determined based on a variety of relevant factors, including but not limited to, qualifications, experience, geographic location and other relevant skills.
At the heart of our Total Rewards commitment is a comprehensive, flexible and competitive benefits program for eligible positions that enables you to choose the plans and coverage that meet your personal needs. This includes above\-market, diverse and robust health and insurance benefits to meet the varied needs of our employees as well as a significant 401(k) matching contribution to help our employees save for retirement. We also promote employee well\-being with programs that help you enjoy your career alongside life’s many other commitments and opportunities.
Amneal is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, national origin, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable federal, state, or local laws. If you require a reasonable accommodation to complete the application process or to participate in any part of the hiring process, please contact us at [email protected] . Requests will be handled confidentially and in accordance with applicable laws.
Salary Context
This $240K-$270K range is above the 75th percentile for AI Architect roles in our dataset (median: $181K across 29 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At Amneal Pharmaceuticals, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $237,300 based on 102 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($255K) sits 7% above the category median. Disclosed range: $240K to $270K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Amneal Pharmaceuticals AI Hiring
Amneal Pharmaceuticals has 1 open AI role right now. They're hiring across AI Architect. Based in Bridgewater, NJ, US. Compensation range: $270K - $270K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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