Worldwide Specialist Solutions Architect - AI, Data & AI GTM
Amazon
- Location
- Onsite (New York, New York · East Palo Alto, California · Chicago, Illinois · Mountain View, California)
- Compensation
- $153k - $239k/yr
- Employment
- Full-time
- Level
- Senior Level
Posted 2 days ago
About the Role
AWS is seeking a Machine Learning Solutions Architect to serve as a technical authority on model customization and inference. You will partner with customers across the Americas to design scalable ML architectures using Amazon SageMaker and purpose-built silicon, driving product improvements and accelerating AI adoption.
Skills
Machine learning
Generative AI
Amazon SageMaker
Cloud computing
Distributed training
Model fine-tuning
Inference optimization
Architecture design
Python
Data science
MLOps
Technical leadership
Customer engagement
System engineering
Infrastructure
GPU computing
Benefits
- Health insurance
- Dental insurance
- Vision insurance
- 401(k) matching
- Paid time off
- Parental leave
- Life insurance
Perks
- Restricted stock units
- Sign-on payments
Full job details
Generative AI and large-scale machine learning are redefining what's possible — and AWS is at the center of that transformation. We are looking for a Machine Learning Solutions Architect (ML SA) who will serve as the technical authority on model customization and inference to help customers across the AMERICAS unlock the full potential of foundation models, custom training, and production-scale serving on AWS.
Amazon has invested in AI for over two decades. From the recommendation engines that power Amazon.com to the deep learning behind Alexa, Prime Air, Amazon Go, and our supply chain optimization — machine learning is embedded in everything we build. Now, through Amazon SageMaker AI, SageMaker HyperPod, Amazon Bedrock, and our purpose-built silicon (Trainium, Inferentia), we are enabling customers to fine-tune, train, and deploy models at unprecedented scale and efficiency.
As a Model Customization & Inference SageMaker ML SA, you will work directly with customers — from startups to enterprises — to design end-to-end ML architectures that span the full lifecycle: data preparation, distributed training, model fine-tuning (LoRA, PEFT, RLHF), inference optimization, and production deployment. You will operate across all 2 layers of the AWS AI/ML stack:
Infrastructure & Compute — SageMaker HyperPod, GPU-based EC2, EKS/ECS for ML and Gen AI workloads
ML Platforms — Amazon SageMaker AI (training jobs, endpoints, pipelines, MLOps)
You will be the bridge between customers and AWS engineering — translating real-world business problems into scalable ML architectures and feeding critical customer signals back to service teams to shape the product roadmap.
Key job responsibilities
Solution Design & Delivery: Partner with customers' data science and engineering teams to deeply understand their business objectives, then architect solutions that leverage AWS AI/ML services — with emphasis on model customization (fine-tuning, continued pre-training, distillation) and inference optimization (model compilation, quantization, endpoint auto-scaling, multi-model endpoints).
Technical Leadership: Serve as the go-to SME on model customization and inference patterns across SageMaker AI and SageMaker HyperPod. Guide field SAs and customers on best practices for training at scale and deploying models with optimal latency, throughput, and cost.
Customer Adoption & Revenue Impact: Partner with Specialist SAs, Account Teams, Sales, and Business Development to accelerate adoption of SageMaker AI across the AMERICAS — directly contributing to pipeline generation, opportunity progression, and revenue attainment.
Thought Leadership & Evangelism: Author technical blogs, whitepapers, reference architectures, and reusable solution artifacts. Deliver presentations at flagship events (AWS re:Invent, AWS Summits, industry conferences) to establish AWS as the leader in model customization and inference.
Voice of the Customer: Act as the technical liaison between customers and AWS service teams (SageMaker). Capture and escalate product feature requests, identify gaps, and drive platform improvements grounded in real-world customer needs.
Community Building: Develop and scale an internal community of ML subject matter experts across the AMERICAS, fostering knowledge sharing on model customization, inference optimization, and emerging ML patterns.
A day in the life
Your day starts with a whiteboard session alongside a financial services customer's ML engineering team, walking them through a distributed fine-tuning architecture — helping them set up Supervised Fine-Tuning (SFT) with LoRA on a Llama model using SageMaker Training Jobs across a cluster of P5e instances, configuring FSDP for efficient multi-GPU parallelism, and advising them on checkpointing strategies so they can resume training without losing hours of compute. By midday, you're on a call with a retail customer who's struggling with inference latency on their real-time recommendation model — you dig into their endpoint configuration, recommend migrating to a SageMaker real-time inference endpoint backed by GPU or custom chips,and help them benchmark quantized vs. full-precision serving to hit their P99 latency targets. After lunch, you carve out time to author a reference architecture blog on multi-model endpoints for generative AI workloads, review a Product Feature Request (PFR) you're drafting based on customer feedback around SageMaker HyperPod training job scheduling, and jump into a Slack thread with the service team to advocate for a customer-requested enhancement to Serverless Model Customization. You close the day prepping a re:Invent chalk talk on cost-optimized inference patterns — pulling real customer benchmarks, tuning your demo notebook, and syncing with your coverage SA on an upcoming Bedrock-to-SageMaker migration opportunity that could unlock a six-figure SageMaker pipeline. No two days look the same, but every day centers on one thing: helping customers get from raw model to production-grade, cost-efficient inference — faster.
About the team
The Worldwide Specialist Organization (WWSO) SageMaker AI team is a group of deeply technical Solutions Architects, Data Scientists, and ML Engineers who serve as the global technical authority on Amazon SageMaker AI. We sit at the intersection of customers and product — working hands-on with enterprises across every industry to design and deliver end-to-end ML solutions spanning model customization, distributed training, inference optimization, and MLOps at scale. Our charter is threefold: build reusable reference architectures and solutions that act as force multipliers for the field, drive specialist customer engagements on the most complex and high-impact ML workloads, and shape the SageMaker AI product roadmap by translating real-world customer signals into product priorities. Basic Qualifications: - 3+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience
- 3+ years of design, implementation, or consulting in applications and infrastructures experience
- 5+ years of IT development or implementation/consulting in the software or Internet industries experience Preferred Qualifications: - 5+ years of design, implementation, or consulting in applications and infrastructures experience
- Experience working with end user or developer communities
- 3+ years of IT development or implementation/consulting in the software or Internet industries experience
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
USA, CA, East Palo Alto - 176,600.00 - 239,000.00 USD annually
USA, CA, Mountain View - 176,600.00 - 239,000.00 USD annually
USA, IL, Chicago - 153,600.00 - 207,800.00 USD annually
USA, NY, NEW YORK - 169,000.00 - 228,600.00 USD annually
Amazon has invested in AI for over two decades. From the recommendation engines that power Amazon.com to the deep learning behind Alexa, Prime Air, Amazon Go, and our supply chain optimization — machine learning is embedded in everything we build. Now, through Amazon SageMaker AI, SageMaker HyperPod, Amazon Bedrock, and our purpose-built silicon (Trainium, Inferentia), we are enabling customers to fine-tune, train, and deploy models at unprecedented scale and efficiency.
As a Model Customization & Inference SageMaker ML SA, you will work directly with customers — from startups to enterprises — to design end-to-end ML architectures that span the full lifecycle: data preparation, distributed training, model fine-tuning (LoRA, PEFT, RLHF), inference optimization, and production deployment. You will operate across all 2 layers of the AWS AI/ML stack:
Infrastructure & Compute — SageMaker HyperPod, GPU-based EC2, EKS/ECS for ML and Gen AI workloads
ML Platforms — Amazon SageMaker AI (training jobs, endpoints, pipelines, MLOps)
You will be the bridge between customers and AWS engineering — translating real-world business problems into scalable ML architectures and feeding critical customer signals back to service teams to shape the product roadmap.
Key job responsibilities
Solution Design & Delivery: Partner with customers' data science and engineering teams to deeply understand their business objectives, then architect solutions that leverage AWS AI/ML services — with emphasis on model customization (fine-tuning, continued pre-training, distillation) and inference optimization (model compilation, quantization, endpoint auto-scaling, multi-model endpoints).
Technical Leadership: Serve as the go-to SME on model customization and inference patterns across SageMaker AI and SageMaker HyperPod. Guide field SAs and customers on best practices for training at scale and deploying models with optimal latency, throughput, and cost.
Customer Adoption & Revenue Impact: Partner with Specialist SAs, Account Teams, Sales, and Business Development to accelerate adoption of SageMaker AI across the AMERICAS — directly contributing to pipeline generation, opportunity progression, and revenue attainment.
Thought Leadership & Evangelism: Author technical blogs, whitepapers, reference architectures, and reusable solution artifacts. Deliver presentations at flagship events (AWS re:Invent, AWS Summits, industry conferences) to establish AWS as the leader in model customization and inference.
Voice of the Customer: Act as the technical liaison between customers and AWS service teams (SageMaker). Capture and escalate product feature requests, identify gaps, and drive platform improvements grounded in real-world customer needs.
Community Building: Develop and scale an internal community of ML subject matter experts across the AMERICAS, fostering knowledge sharing on model customization, inference optimization, and emerging ML patterns.
A day in the life
Your day starts with a whiteboard session alongside a financial services customer's ML engineering team, walking them through a distributed fine-tuning architecture — helping them set up Supervised Fine-Tuning (SFT) with LoRA on a Llama model using SageMaker Training Jobs across a cluster of P5e instances, configuring FSDP for efficient multi-GPU parallelism, and advising them on checkpointing strategies so they can resume training without losing hours of compute. By midday, you're on a call with a retail customer who's struggling with inference latency on their real-time recommendation model — you dig into their endpoint configuration, recommend migrating to a SageMaker real-time inference endpoint backed by GPU or custom chips,and help them benchmark quantized vs. full-precision serving to hit their P99 latency targets. After lunch, you carve out time to author a reference architecture blog on multi-model endpoints for generative AI workloads, review a Product Feature Request (PFR) you're drafting based on customer feedback around SageMaker HyperPod training job scheduling, and jump into a Slack thread with the service team to advocate for a customer-requested enhancement to Serverless Model Customization. You close the day prepping a re:Invent chalk talk on cost-optimized inference patterns — pulling real customer benchmarks, tuning your demo notebook, and syncing with your coverage SA on an upcoming Bedrock-to-SageMaker migration opportunity that could unlock a six-figure SageMaker pipeline. No two days look the same, but every day centers on one thing: helping customers get from raw model to production-grade, cost-efficient inference — faster.
About the team
The Worldwide Specialist Organization (WWSO) SageMaker AI team is a group of deeply technical Solutions Architects, Data Scientists, and ML Engineers who serve as the global technical authority on Amazon SageMaker AI. We sit at the intersection of customers and product — working hands-on with enterprises across every industry to design and deliver end-to-end ML solutions spanning model customization, distributed training, inference optimization, and MLOps at scale. Our charter is threefold: build reusable reference architectures and solutions that act as force multipliers for the field, drive specialist customer engagements on the most complex and high-impact ML workloads, and shape the SageMaker AI product roadmap by translating real-world customer signals into product priorities. Basic Qualifications: - 3+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience
- 3+ years of design, implementation, or consulting in applications and infrastructures experience
- 5+ years of IT development or implementation/consulting in the software or Internet industries experience Preferred Qualifications: - 5+ years of design, implementation, or consulting in applications and infrastructures experience
- Experience working with end user or developer communities
- 3+ years of IT development or implementation/consulting in the software or Internet industries experience
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. 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.
USA, CA, East Palo Alto - 176,600.00 - 239,000.00 USD annually
USA, CA, Mountain View - 176,600.00 - 239,000.00 USD annually
USA, IL, Chicago - 153,600.00 - 207,800.00 USD annually
USA, NY, NEW YORK - 169,000.00 - 228,600.00 USD annually