Delivery Consultant - AI/ML, AWS Professional Services WWPS Healthcare and Life Science

We need to output HTML fragment with sections: About the role (maybe from description), Responsibilities, Requirements (Basic Qualifications), Preferred Qualifications (maybe as Skills?), Benefits, Pay, Schedule if present. The posting includes Pay (salary ranges) and Benefits (mention of benefits). No schedule info. So we include Pay and Benefits sections. We must not repeat job title as heading. No Job ID. Remove boilerplate: application deadline, EEO, etc. Also remove duplicated lines like "Key job responsibilities" repeated. Keep substantive info. We need to use

for section headings. Use

• for any list of duties/requirements/benefits. Use

  for prose. We need to preserve all substantive info; not invent or summarize away detail. So we need to include the description paragraphs as prose under About the role maybe. Responsibilities list from bullet points (the "Key job responsibilities" list). Requirements: Basic Qualifications list. Preferred Qualifications list maybe as Skills or Preferred Qualifications section. Benefits: list of benefits described. Pay: list of salary ranges per location. We must not include sections with filler like "commensurate with experience". Not present. We must not include navigation/legal/EEO etc. So remove lines about Amazon is an equal opportunity employer, Los Angeles County applicants, etc. Also remove "Application deadline: Sep 27, 2026". Remove "Why AWS?" etc? That's substantive? It's about AWS culture; maybe we can include under About the Team? But we only have sections we decide. The instruction: include a section ONLY when the posting has real, specific content for it. We can have About the role (description), Responsibilities, Requirements, Skills (maybe Preferred Qualifications), Benefits, Pay. The "About The Team" section includes diverse experiences, why AWS, inclusive team culture, mentorship, work/life balance. That's substantive; we could include as "About the Team" section. But the instruction says use headings like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. It didn't list About the Team but we could still include if content exists. However they said "use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." The e.g. suggests those are examples; we can use others if needed? Safer to stick to those headings. We could incorporate team info into About the role or maybe under Requirements? Better to put under About the role prose. Thus sections: About the role (prose from initial paragraphs and maybe team info). Responsibilities (ul). Requirements (ul) from Basic Qualifications. Skills (ul) from Preferred Qualifications (maybe treat as Skills). Benefits (ul) from benefits description. Pay (ul) from salary ranges. We must not include schedule. Now extract content. About the role: The initial paragraph: "Are you excited about building software solutions around large, complex Machine Learning (ML) and Artificial Intelligence (AI) systems? Want to help the largest global enterprises derive business value through the adoption and automation of Generative AI (GenAI)? Excited by using massive amounts of disparate data to develop AI/ML models? Eager to learn to apply AI/ML to a diverse array of enterprise use? Thrilled to be a key part of Amazon, who has been investing in Machine Learning for decades - pioneering and shaping the world’s AI technology? The Amazon Web Services Professional Services (ProServe) team is seeking a skilled ML Engineer to join our team as a Delivery Consultant at Amazon Web Services (AWS). In this role, you'll work closely with customers to design, implement, and manage AWS AI/ML and GenAI solutions that meet their technical requirements and business objectives. You'll be a key player in driving customer success through their cloud journey, providing technical expertise and best practices throughout the ML project lifecycle. Possessing a deep understanding of AWS products and services, as a Delivery Consultant you will be proficient in architecting complex, scalable, and secure AI/ML and GenAI solutions tailored to meet the specific needs of each customer. You’ll work closely with stakeholders to gather requirements, assess current infrastructure, and propose effective migration strategies to AWS. As trusted advisors to our customers, providing guidance on industry trends, emerging technologies, and innovative solutions, you will be responsible for leading the implementation process, ensuring adherence to best practices, optimizing performance, and managing risks throughout the project. The AWS Professional Services organization is a global team of experts that help customers realize their desired business outcomes when using the AWS Cloud. We work together with customer teams and the AWS Partner Network (APN) to execute enterprise cloud computing initiatives. Our team provides assistance through a collection of offerings which help customers achieve specific outcomes related to enterprise cloud adoption. We also deliver focused guidance through our global specialty practices, which cover a variety of solutions, technologies, and industries." Then later there is "About The Team Diverse Experiences..." etc. We could incorporate that as part of About the role prose. We'll combine all that into a few paragraphs. Now Responsibilities: list from "Key job responsibilities As an experienced technology professional, you will be responsible for:" then bullet points: - Implementing end-to-end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment and monitoring - Designing and implementing machine learning pipelines that support high-performance, reliable, scalable, and secure ML workloads - Designing scalable ML solutions and operations (MLOps) using AWS services and leveraging GenAI solutions when applicable - Collaborating with cross-functional teams (Applied Science, DevOps, Data Engineering, Cloud Infrastructure, Applications) to prepare, analyze, and operationalize data and AI/ML models - Serving as a trusted advisor to customers on AI/ML and GenAI solutions and cloud architectures - Sharing knowledge and best practices within the organization through mentoring, training, publication, and creating reusable artifacts - Ensuring solutions meet industry standards and supporting customers in advancing their AI/ML, GenAI, and cloud adoption strategies - This is a customer-facing role with potential travel to customer sites as needed. We'll put each as

  . Requirements (Basic Qualifications): - 3+ years of software development, or 3+ years of scripting language experience - 3+ years of building machine learning and generative AI models for business application experience - Experience with cloud services related to machine learning (e.g., Amazon SageMaker) and generative AI applications We'll list as

  . Skills (Preferred Qualifications): - Knowledge of AWS services including compute, storage, networking, security, databases, machine learning, and serverless technologies - Knowledge of AWS services including SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch and AWS certifications - 2+ years of experience with design, deployment, and evaluation of AI agents and orchestration approaches; experience with open source frameworks like LangChain, LangGraph, LlamaIndex, and/ or similar tools - 3+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience using PyTorch or TensorFlow - Experience building ML pipelines with MLOps best practices, including: data preprocessing, distributed & GPU training, model deployment, monitoring, and retraining; experience with container and CI/CD pipelines - Domain expertise in healthcare and life sciences We'll list as

  . Benefits: The paragraph: "Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits." Also earlier: "Your Amazon package will include sign-on payments and restricted stock units (RSUs)." That's part of compensation but not benefits; we could include under Benefits or Pay? Probably Benefits includes sign-on payments and RSUs? Might be compensation. We'll include under Benefits: sign-on payments and RSUs, health insurance etc., 401(k) matching, paid time off, parental leave. We'll list as

  . Pay: The salary ranges list for various locations. We'll list each as

  with location and range. We must not include duplicated lines. Ensure we preserve all. Now produce HTML:

  About the role

  ... combined prose ...

  Responsibilities

  • ...
  ...

  Requirements

  ...

  Skills

  ...

  Benefits

  ...

  Pay

  ...

  Make sure no extra whitespace? It's fine. Now craft the prose for About the role. Combine initial paragraphs and About The Team content. Let's draft: "Are you excited about building software solutions around large, complex Machine Learning (ML) and Artificial Intelligence (AI) systems? Want to help the largest global enterprises derive business value through the adoption and automation of Generative AI (GenAI)? Excited by using massive amounts of disparate data to develop AI/ML models? Eager to learn to apply AI/ML to a diverse array of enterprise use? Thrilled to be a key part of Amazon, who has been investing in Machine Learning for decades - pioneering and shaping the world’s AI technology? The Amazon Web Services Professional Services (ProServe) team is seeking a skilled ML Engineer to join our team as a Delivery Consultant at Amazon Web Services (AWS). In this role, you'll work closely with customers to design, implement, and manage AWS AI/ML and GenAI solutions that meet their technical requirements and business objectives. You'll be a key player in driving customer success through their cloud journey, providing technical expertise and best practices throughout the ML project lifecycle. Possessing a deep understanding of AWS products and services, as a Delivery Consultant you will be proficient in architecting complex, scalable, and secure AI/ML and GenAI solutions tailored to meet the specific needs of each customer. You’ll work closely with stakeholders to gather requirements, assess current infrastructure, and propose effective migration strategies to AWS. As trusted advisors to our customers, providing guidance on industry trends, emerging technologies, and innovative solutions, you will be responsible for leading the implementation process, ensuring adherence to best practices, optimizing performance, and managing risks throughout the project. The AWS Professional Services organization is a global team of experts that help customers realize their desired business outcomes when using the AWS Cloud. We work together with customer teams and the AWS Partner Network (APN) to execute enterprise cloud computing initiatives. Our team provides assistance through a collection of offerings which help customers achieve specific outcomes related to enterprise cloud adoption. We also deliver focused guidance through our global specialty practices, which cover a variety of solutions, technologies, and industries. AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (diversity) conferences, inspire us to never stop embracing our uniqueness. We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive