GenAI/ML Specialist Solutions Architect, AGS Specialist Solutions Architects

We need to output HTML fragment with headings

for sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. Must not repeat job title. Must not include Job ID etc. Must preserve all substantive info, no invented or summarizing away detail. Remove boilerplate, navigation, legal, EEO, equal-opportunity, scam-warning, duplicated lines, application instructions. Also remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. We need to parse the description and extract sections. The posting includes: - Description (intro) about AWS Global Sales, AI/ML etc. This could be "About the role" maybe. - Then "Key job responsibilities" list. - Then "About The Team" with subpoints: Diverse Experiences, Why AWS?, Inclusive Team Culture, Mentorship & Career Growth, Work/Life Balance. - Then "Basic Qualifications" and "Preferred Qualifications". - Then "Benefits" mention: base salary range, sign-on payments, RSUs, comprehensive benefits including health insurance, 401(k) matching, paid time off, parental leave. Also link. - Also "Travel up to 50% across the USA may be possible." That could be Schedule? Or maybe "Schedule" section. - Also location list: "If you are a qualified and accepted candidate, you may work out of any of the following cities: San Jose, Los Angeles, Denver, Chicago, Austin, Dallas, Atlanta, Arlington." That's location, maybe not a required section; could be part of About the role? Not a defined heading; we could include under About the role or maybe as a separate "Location" but not in allowed headings. Since only allowed headings are those listed, we should not create new headings. So location info could be placed in About the role paragraph. - Also "Travel up to 50% across the USA may be possible." Could go under Schedule? Or maybe under Responsibilities? Travel is not a responsibility but maybe schedule. Since we have Schedule heading allowed, we can include travel info there. - Pay: base salary ranges listed for various locations. That is Pay section. - Benefits: health insurance etc. - Skills: maybe not explicitly listed; but we could infer from qualifications: deep technical experience with GenAI, ML, DL, mathematics/statistics, familiarity with ecosystem of software vendors in GenAI space. Could be under Skills. - Requirements: maybe Basic Qualifications. - Qualifications: Preferred Qualifications. - About the role: initial description. We must not create sections with filler like "commensurate with experience". We have actual content. We need to output only HTML fragment, no /, no markdown fences. We must use

for section headings,

• for any list of duties/requirements/benefits, and

  for prose. We need to preserve ALL substantive information; do not invent or summarize away detail. So we must keep details like specific services: SageMaker, Amazon Bedrock, Amazon AgentCore, Amazon Rekognition, Amazon Transcribe, Amazon Quick, Amazon Personalize, etc. Also mention "Amazon Quick" maybe typo for Amazon QuickSight? Keep as is. We must not repeat job title as heading. The title is "Machine Learning Solutions Architect (ML SA)". Not to be used as heading. We must not put Job ID/requisition numbers as headings; we can drop them. We must remove boilerplate: EEO, equal opportunity, legal, accommodation links, etc. Also remove duplicated lines. We need to be careful to not include the "Amazon is an equal opportunity employer..." lines. Also remove the Los Angeles County applicants text, San Francisco Fair Chance Ordinance, etc. Also remove the accommodation link, etc. Also remove the "Learn more about our benefits at https://amazon.jobs/en/benefits." maybe keep as part of Benefits? It's a link; but it's not boilerplate? It's a reference to benefits page; could be considered part of Benefits. However it's a link; we could keep it as part of Benefits prose. But it's not essential; but we can keep. We also need to remove the "Company - Amazon Web Services, Inc." line maybe not needed. We need to keep the base salary ranges for each location; that is Pay. We need to keep the benefits description. We need to keep the travel up to 50% across the USA. We need to keep the list of cities for possible work locations. We need to keep the "If you are a qualified and accepted candidate, you may work out of any of the following cities: San Jose, Los Angeles, Denver, Chicago, Austin, Dallas, Atlanta, Arlington." That's location info. We need to keep the "Travel up to 50% across the USA may be possible." Now structure:

  About the role

  ... (intro paragraph plus location and travel maybe?)

  But we have location and travel maybe better under Schedule? Travel is schedule-related. Location maybe not a heading; could be part of About the role. We need to decide. Let's extract the intro paragraph: "Description AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and comprehensive support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success. This customer-first approach is how we built the world's most adopted cloud. Join us and help us grow. Are you passionate about Artificial Intelligence, Machine Learning, and GenerativeAI? Are you passionate about helping customers build solutions leveraging the state-of-the-art AI/ML/GenAI tools on Amazon Web Service (AWS)? Come join us! At Amazon, we’ve been investing deeply in artificial intelligence for over 20 years, and many of the capabilities customers experience in our products are driven by machine learning. Amazon.com’s recommendations engine is driven by machine learning (ML), as are the paths that optimize robotic picking routes in our fulfillment centers. Our supply chain, forecasting, and capacity planning are also informed by ML algorithms. Alexa is fueled by Natural Language Understanding and Automated Speech Recognition deep learning; as is Prime Air, and the computer vision technology in our new retail experience, Amazon Go. We have thousands of engineers at Amazon committed to machine learning and deep learning, and it’s a big part of our heritage. Within AWS, we’re focused on bringing that knowledge and capability to customers through layers of the AI stack: 1) API-driven Inference Services like Amazon Bedrock 2) Agentic AI including Agentic platforms, tool usage, evaluations, governance and cost management along with use of Agentic SDKs such as Strands and platforms such as Amazon Bedrock AgentCore 3) Frameworks and Infrastructure with tools like Pytorch and TensorFlow, 2) Machine Learning Platforms such as Amazon SageMaker for data scientists, and, k to quickly add intelligence to applications with simple API calls. AWS is looking for a Machine Learning Solutions Architect (ML SA), who will be the Subject Matter Expert (SME) for helping customers in the AMERICAS design solutions that leverage our ML services. As part of the team, you will work closely with ISV(Independent Software Vendors) to enable large-scale use cases, design ML pipelines, and drive the adoption of AWS for the AI/ML platforms. You will interact with other SAs in the field, providing guidance on their customer engagements, and you will develop white papers, blogs, reference implementations, and presentations to enable customers to fully leverage AI/ML on AWS. Additionally, as the voice of the customer, you will work closely with the service teams, and submit product feature requests to drive the platform forward. You must have deep technical experience working with technologies related to Generative AI, machine learning and/or deep learning. A strong mathematics and statistics background is preferred in addition to experience building complex machine learning models. You will be familiar with the ecosystem of software vendors in the GenAI space, and will leverage this knowledge to help AWS customers in their selection process." That is a long paragraph; we can keep as prose. Then location and travel: "If you are a qualified and accepted candidate, you may work out of any of the following cities: San Jose, Los Angeles, Denver, Chicago, Austin, Dallas, Atlanta, Arlington. Travel up to 50% across the USA may be possible." We could include that in About the role as well, or maybe separate Schedule section for travel. Since Schedule heading is allowed, we could put travel info there. Location maybe not needed as separate heading; could be part of About the role. Now Responsibilities: "Key job responsibilities Working with customers’ development and data science teams to deeply understand their business and technical needs. After understanding their needs, you will design solutions that make the best use of the AWS cloud platform and AWS AI/ML Services including SageMaker, Amazon Bedrock, Amazon AgentCore, Amazon Rekognition, Amazon Transcribe, Amazon Quick, Amazon Personalize, and other AI/ML services. Partner with SAs, Sales, Business Development and the AI/ML Service teams to accelerate customer adoption and revenue attainment in the AMERICAS for Amazon SageMaker and related services that support GenAI use cases. Thought Leadership – Evangelize AWS ML services and share best practices through forums such as AWS blogs, whitepapers, reference architectures and public-speaking events such as AWS Summit, AWS re:Invent, etc. Act as a technical liaison between customers and the AWS SageMaker or other service teams to provide customer driven product improvement feedback. Develop and support an AWS internal community of GenAI related subject matter experts in the AMERICAS." We need to turn each sentence into list items? The instruction: Use
  • for any list of duties/requirements/benefits. So we should make a
    with each duty as
    • . Could also keep prose if not list? But they said for any list of duties/requirements/benefits use
      • . So we should convert to list. Now About The Team: This includes multiple subpoints: Diverse Experiences, Why AWS?, Inclusive Team Culture, Mentorship & Career Growth, Work/Life Balance. These could be considered as sections but we don't have headings for them; we could incorporate them under About the role or maybe under a generic "About the team" but not allowed heading. Since only allowed headings are those listed, we should not create new headings like "About the Team". So we need to fold that content into existing sections, perhaps About the role or maybe under a generic

        without heading? But we need to output only HTML fragment; we can include

        paragraphs without heading. That's fine. So we can include those paragraphs as prose under maybe after About the role heading. But we must not create a heading for About The Team. So we will just add

        paragraphs for each bullet. Now Basic Qualifications and Preferred Qualifications: Those correspond to Requirements and Qualifications sections. We'll make

        Requirements

        with
        • for each basic qualification. And

          Qualifications

          for preferred qualifications. Now Benefits: We have benefits description: base salary range (but that's Pay), sign-on payments and RSUs, 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. Also link to learn more. We should separate Pay and Benefits. Pay section: base salary ranges for various locations. Benefits section: the rest. Now Schedule: travel up to 50% across the USA may be possible. Could also include maybe typical schedule? Not given. So we will put travel info there. Now Skills: maybe we can extract from qualifications: deep technical experience with GenAI, ML, DL; strong mathematics and statistics background; experience building complex ML models; familiarity with