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About This Role
United States \- Remote
JOB ID: R\-250802 LOCATION: United States \- Remote WORK LOCATION TYPE: Remote DATE POSTED: Aug. 03, 2026 CATEGORY: Information Systems SALARY RANGE: 120,677\.05USD \-163,268\.95 USD
Join Amgen’s Mission of Serving Patients
At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.
Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. Amgen is advancing a broad and deep pipeline of medicines to treat cancer, heart disease, inflammatory conditions, rare diseases, and obesity and obesity\-related conditions. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.
Our award\-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.
Business Architect, Applied AI \& Automation
What you will do
Let’s do this. Let’s change the world. The Business Architect, Applied AI \& Automation position offers a unique opportunity to join a fun, innovative team within the AI \& Data Science organization. This role will support next\-generation AI, automation, process intelligence, digital workflow, and productivity capabilities across Amgen's Applied AI \& Automation portfolio.
The job offers a unique opportunity to join a fun, innovative engineering team within the AI \& Data Science (AI\&D) \- organization. We are the Applied AI team (AI Studio). AI Studio is Amgen’s enterprise engine for turning high\-value business challenges into scalable AI products. We partner with key business partners across the company to identify the right opportunities, shape them into actionable use cases, and design, build, and launch AI products responsibly. Our work spans the full lifecycle—from early discovery and rapid prototyping to production deployment, reuse across the enterprise, and measurable business impact.
This is a pre\-delivery business architecture role, not a traditional Technical Business Analyst role. The primary focus is to shape business demand before technical design and delivery by partnering with business stakeholders, product teams, solution partners, and delivery architects to clarify opportunities, define business value, document business processes, and prepare high\-quality demand for prioritization and technical review.
The role is responsible for leading discovery activities, capturing business context, defining value drivers, supporting business cases, documenting current and future\-state processes, and ensuring opportunities are ready to move through intake, discovery, technical review, prioritization, and delivery planning. This role helps ensure that AI and automation investments are connected to measurable business outcomes and that delivery teams receive clear, complete, and value\-aligned opportunities.
The Business Architect will play a critical role in improving portfolio quality, strengthening prioritization decisions, and supporting value\-driven delivery. The AI\&DS framework separates Must Do work from discretionary value\-driven work and uses structured inputs such as strategic value, product value, release value, reputational risk, urgency, severity, dependency, release duration, and resource allocation to support objective decision\-making.
Roles \& Responsibilities:
- Lead business discovery activities for AI, automation, process, workflow, and digital opportunities across Applied AI \& Automation.
- Partner with business stakeholders, submitters, Product Managers, Solution Partners, Delivery Architects, and Delivery Leads to clarify business needs, opportunity scope, expected outcomes, and readiness for technical review.
- Translate ambiguous business requests into structured opportunity statements, including business problem, stakeholder impact, current\-state pain points, desired future state, value drivers, risks, dependencies, assumptions, and success measures.
- Support intake triage by assessing whether requests are clear, complete, aligned to business priorities, and ready to move into discovery.
- Capture the voice of the customer and translate stakeholder input into business architecture artifacts, including opportunity briefs, process maps, value summaries, business case inputs, decision narratives, and discovery documentation.
- Define and document current\-state and future\-state business processes using fit\-for\-purpose methods such as process flows, SIPOC diagrams, value stream maps, journey maps, capability maps, or decision flows.
- Identify process inefficiencies, gaps, handoffs, dependencies, risks, business rules, and opportunities for AI, automation, or process improvement.
- Partner with stakeholders to identify expected value drivers, including productivity, cost avoidance, quality and compliance, customer experience, employee experience, speed\-to\-patient, revenue growth, sustainability, or transformation enablement.
- Support the development of business cases by documenting value assumptions, expected adoption, measurable outcomes, and the logic connecting business activity to value.
- Help distinguish between product\-level value, release\-level value, new value, and sustained value to support portfolio planning and value tracking.
- Collect, document, and validate business\-side prioritization inputs, including strategic value, product value, release value, value confidence, reputational risk, dependency, severity, urgency context, and business resource commitments.
- Prepare opportunities for Technical Review by ensuring business context, scope, desired outcomes, data/content sources, access/security needs, dependencies, SMEs, sample use cases, and business points of contact are clearly documented.
- Partner with Delivery Architects to identify gaps before technical feasibility assessment and support a clean handoff from discovery into technical design and delivery planning.
- Create leadership\-ready summaries that explain the business problem, value driver, recommendation, scope, risks, tradeoffs, and next steps.
- Support prioritization conversations by helping stakeholders understand how value, urgency, risk, strategic alignment, and effort influence portfolio decisions.
- Use approved AI tools, including ChatGPT and other AI technologies, to accelerate discovery, summarize stakeholder input, improve documentation quality, structure business cases, and create reusable business process artifacts.
- Collaborate across global, virtual, and cross\-functional teams to align business, product, architecture, and delivery stakeholders around clear outcomes.
- Promote continuous improvement in intake, discovery, business architecture, value definition, and pre\-delivery readiness practices.
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The professional we seek is an individual with these qualifications.
Basic Qualifications:
Doctorate degree
OR
Master’s degree and 2 years of Computer Science, IT or related field
OR
Bachelor’s degree and 4 years of Computer Science, IT or related field
OR
Associate’s degree and 8 years of Computer Science, IT or related field
OR
High school diploma / GED and 10 years of Computer Science, IT or related field
Preferred Qualifications:
Functional Skills:
Must\-Have Skills:
- Strong business architecture, business analysis, and problem\-framing skills.
- Experience leading discovery activities with business stakeholders and translating ambiguous needs into structured, decision\-ready opportunities.
- Strong understanding of demand intake, opportunity assessment, discovery, prioritization, technical review preparation, and pre\-delivery planning.
- Ability to define and document business problems, stakeholder needs, current\-state processes, future\-state processes, value drivers, assumptions, risks, dependencies, and expected outcomes.
- Experience supporting business cases, value assessments, ROI estimates, benefit hypotheses, or value realization planning.
- Strong process documentation skills, including current\-state and future\-state process mapping.
- Ability to simplify complex business problems into clear opportunity statements, process flows, business narratives, and leadership\-ready recommendations.
- Strong stakeholder engagement skills, including the ability to facilitate alignment across business, product, architecture, solution, and delivery teams.
- Ability to communicate clearly and confidently with business stakeholders, product teams, architects, delivery teams, and leadership.
- Experience preparing executive summaries, discovery readouts, prioritization recommendations, or business\-facing presentations.
- Strong analytical thinking, structured problem\-solving, and ability to validate assumptions using available data and stakeholder input.
- Ability to use approved AI tools to improve discovery outputs, summarize information, structure documentation, draft business cases, and increase speed and quality of business architecture work.
- Ability to operate independently in a matrixed environment with multiple stakeholders, priorities, and discovery efforts.
Good\-to\-Have Skills:
- Experience with AI, automation, process intelligence, digital workflow, enterprise platforms, or transformation initiatives.
- Familiarity with technologies such as UiPath, DocuSign, Celonis, Power Platform, WalkMe, Custom GPTs, ChatGPT, or similar AI and automation technologies.
- Experience with value frameworks, prioritization frameworks, WSJF, portfolio governance, PI planning, or product\-led operating models.
- Familiarity with process methods and tools such as SIPOC, value stream mapping, BPMN, journey mapping, capability mapping, process capability analysis, Miro, Lucidchart, Visio, or similar tools.
- Experience working in Agile, SAFe, Scrum, or product\-led delivery environments.
- Familiarity with Jira, Jira Align, Smartsheet, Confluence, or similar product and portfolio management platforms.
- Familiarity with GxP, CFR 21 Part 11, computer system validation, quality systems, or regulated Life Sciences environments.
- Domain knowledge in one or more functional areas such as Commercial, Operations, R\&D, Medical, Finance, HR, Legal, Compliance, Manufacturing, or Supply Chain.
- Experience supporting technical review, solution shaping, delivery readiness, or architecture engagement.
- Professional certifications such as Lean Six Sigma, CBAP, CCBA, ECBA, SAFe, BPM, Business Architecture, Product Management, or related certifications.
Soft Skills:
- Applies structured, evidence\-based thinking to business problems by asking thoughtful questions, validating assumptions, analyzing available data, and using facts to guide recommendations.
- Demonstrates integrity and sound judgment when handling business information, stakeholder input, value assumptions, prioritization recommendations, and sensitive process details.
- Focuses on creating measurable value for patients, staff, and stakeholders by connecting business needs to clear outcomes, value drivers, adoption expectations, and sustainable impact.
- Operates with urgency while maintaining appropriate quality, compliance, ethics, and risk awareness.
- Builds trust through active listening, open dialogue, respectful challenge, and reliable follow\-through.
- Communicates clearly and transparently with business, product, architecture, solution, and delivery stakeholders.
- Collaborates effectively across functions, geographies, and product teams to align stakeholders around business outcomes and delivery readiness.
- Simplifies complex or ambiguous business problems into clear opportunity statements, process flows, value logic, and decision\-ready recommendations.
- Takes accountability for the quality and completeness of discovery outputs, including scope, business context, value assumptions, stakeholders, dependencies, risks, and readiness criteria.
- Demonstrates curiosity and continuous improvement by using approved AI tools, automation methods, and structured documentation practices to improve speed, quality, and consistency.
- Balances progress with discipline by helping teams move quickly without compromising patient impact, ethics, quality, compliance, safety, or business credibility.
Professional Certifications (Preferred but not required):
- Business Architecture certification
- Lean Six Sigma certification
- CBAP, CCBA, ECBA, or other BABOK\-aligned certification
- SAFe certification
- Product Management certification
- BPM or process improvement certification
- AI, automation, or digital transformation certification
Success Measures:
- Improved clarity, completeness, and quality of business demand entering Applied AI \& Automation.
- Stronger discovery outputs that define business problem, scope, process context, stakeholders, value drivers, risks, dependencies, and readiness criteria.
- Better\-prepared opportunities entering Technical Review with fewer gaps in business context, scope, value assumptions, data/content needs, access considerations, and stakeholder commitments.
- Stronger value logic and business case support for AI, automation, and process opportunities.
- More consistent and transparent prioritization inputs across the portfolio.
- Cleaner handoffs from discovery into technical review, delivery planning, and implementation.
- Increased stakeholder confidence in why opportunities are being prioritized and how they connect to measurable business outcomes.
- Greater use of approved AI tools to improve documentation quality, consistency, speed, and reuse.
What you can expect from us
As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well\-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.
The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.
In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include:
- A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
- A discretionary annual bonus program, or for field sales representatives, a sales\-based incentive plan
- Stock\-based long\-term incentives
- Award\-winning time\-off plans
- Flexible work models where possible. Refer to the Work Location Type in the job posting to see if this applies
and make a lasting impact with the Amgen team.
careers.amgen.com
In any materials you submit, you may redact or remove age\-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
Application deadline
Amgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.
Sponsorship
Sponsorship for this role is not guaranteed.
As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease.
Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
Salary Context
This $120K-$163K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amgen, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($141K) sits 34% below the category median. Disclosed range: $120K to $163K.
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.
Amgen AI Hiring
Amgen has 14 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Remote, US, Lisbon, ME, US. Compensation range: $139K - $244K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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