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 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 a customer-obsessed builder passionate about helping enterprise customers achieve their full potential with Generative AI? Do you have deep data science expertise and the technical pre-sales acumen to help customers evaluate, design, and deploy GenAI and ML solutions on AWS? Do you enjoy building impactful AI agents and agentic applications? Join the GenAI/ML Specialist organization as a Senior Generative AI Data Scientist — a senior individual contributor role requiring deep data science expertise, hands-on ML engineering skills, and executive-level customer engagement.

The AGS Specialist organization is part of the customer-facing NAMER sales organization, and is responsible for driving revenue and accelerating adoption of cloud and partner services across diverse customer segments. We work backwards from our customers' most complex and business-critical challenges to develop and execute go-to-market plans that transform ideas into scalable, high-impact businesses. AGS NAMER teams include sales specialists and technical solution architects. As part of the team, you will contribute across the full lifecycle of AWS customer initiatives—from shaping new service and solution concepts to accelerating adoption of established offerings. We pride ourselves on thinking big, delivering exceptional customer outcomes, and collaborating seamlessly across AWS as #OneTeam.

Role Description

In this role, you will be the Subject Matter Expert (SME) for helping NAMER Enterprise customers design and implement Generative AI solutions that leverage Amazon Bedrock. You will translate customer business challenges into data science-driven solutions using AWS. You will define, design, and deploy machine learning models, agentic workflows, and GenAI applications that accelerate adoption of AWS AI/ML services. You will engage with senior engineers, data scientists, product leaders, and executives at strategic enterprise customers to influence technical decisions and provide structured feedback to AWS product teams.

You will interact with customers directly to understand their business problems, help and aid them in implementation of generative AI solutions, deliver briefings and deep dive sessions, and guide customers on adoption patterns and best practices for generative AI. You will build prototypes, proof-of-concepts, and explore novel solutions leveraging Amazon Bedrock, Amazon AgentCore, Strands Agents, and open source frameworks such as LangChain, LangGraph, and CrewAI.

This position will focus on agentic workflows with Amazon Bedrock, including Strands Agents, Bedrock Agents, and open source agentic frameworks. You must have deep technical experience working with technologies related to large language models including LLM architectures, model evaluation, and fine-tuning techniques. You should be proficient with design, deployment, and evaluation of LLM-powered agents, tools, and orchestration approaches.

You will interface with customer data science teams, ML engineering leadership, and C-suite executives to advise on the latest techniques, model architectures, and emerging research. This includes staying current with state-of-the-art approaches from recent publications and research papers — such as advances in reasoning models, multi-agent systems, retrieval-augmented generation, reinforcement learning from human feedback (RLHF), and novel fine-tuning methods — and translating those findings into practical, production-ready solutions for enterprise customers. You will lead technical deep dives and whiteboard sessions with customer chief data scientists and VPs of AI/ML, bridging the gap between research and real-world implementation on AWS.

You should understand the security and compliance requirements for ML/GenAI implementations. You should have experience architecting end-to-end ML/GenAI agentic applications for customers using AWS services and the Well-Architected Framework.

As the ideal candidate, you bring a deep data science background and the business acumen required to lead complex engagements with large enterprises. You have hands-on expertise in statistical modeling, traditional ML, and current areas such as LLMs, RAG, fine-tuning, AI system evaluation, prompt engineering, agents, and AIOps. You are able to credibly advise senior technical and executive stakeholders on architectural trade-offs, best practices, and risk mitigation.

Key job responsibilities
- Working with NAMER Enterprise customers' development and data science teams to deeply understand their business and technical needs. Design and implement solutions that make the best use of the AWS cloud platform and AWS AI/ML services including SageMaker, Amazon Bedrock, Amazon AgentCore, and other AI/ML services.

- Customer Advisor — Implement and deploy state-of-the-art machine learning and Generative AI solutions. Build prototypes, PoCs, and explore new solutions. Interact closely with enterprise customers to accelerate their AI/ML adoption.

- Partner with Data Scientists, SAs, Sales, Business Development, and the AI/ML Service teams to accelerate customer adoption and revenue attainment in NAMER for Amazon Bedrock, SageMaker, and related services that support GenAI use cases.

- Thought Leadership – Evangelize AWS GenAI 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 Amazon Bedrock 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. Create field enablement materials for the broader technical population, to help them understand how to integrate AWS GenAI solutions into customer architectures.

A day in the life
Your day will be dynamic and impactful. You'll engage with enterprise business leaders, dive deep into ML architectures and data science pipelines, and craft transformative AI strategies. You'll collaborate across teams, translating complex statistical and AI concepts into clear, actionable solutions that drive meaningful business outcomes for NAMER Enterprise customers.

About the team
Diverse Experiences

AWS values diverse experiences. Even if you do not meet all of the preferred 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.

Why AWS?

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.

Inclusive Team Culture

AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.

Mentorship & Career Growth

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.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.

Basic qualifications

• 5+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
• 4+ years of data scientist experience
• Experience with statistical models e.g. multinomial logistic regression
• Master's degree or above in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
• 5+ years of experience in end to end technical architecture, design, deployment and operations for Generative AI/ML platforms and applications.
• 3+ year experience working with technologies related to large language models including LLM architectures and model evaluation
• Experience in design/implementation/consulting for Machine Learning/AI/Deep Learning solutions
• Experienced in specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience.

Preferred qualifications

• 2+ years of data visualization using AWS QuickSight, Tableau, R Shiny, etc. experience
• Experience managing data pipelines
• Experience as a leader and mentor on a data science team
• Experience in Kubernetes, Docker or containers ecosystem
• Experience with optimizing ML workloads using Model compression, distillation, pruning, sparsification, quantization, Transformers based algorithms like FlashAttention, PagedAttention, Speculative decoding, Distributed training/inference optimization, Hardware-informed efficient model architecture.
• Experience with open source frameworks for building applications powered by language models like LangChain, LlamaIndex. Design, develop, and optimize high-quality prompts and templates that guide the behavior and responses of LLM.
• Experience with design, deployment, and evaluation of LLM-powered agents and tools and orchestration approachesCustomer facing skills to represent AWS well within the customer’s environment and drive discussions with senior personnel regarding trade-offs, best practices, and risk mitigation.
• Experience with AWS technologies like SageMaker, Step Functions, OpenSearch, PgVector, S3, IAM, Cognito, EC2, Glue, & EMR.

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 - 183,000.00 - 247,600.00 USD annually
USA, CA, Irvine - 159,200.00 - 215,300.00 USD annually
USA, GA, Atlanta - 159,200.00 - 215,300.00 USD annually
USA, IL, Chicago - 159,200.00 - 215,300.00 USD annually
USA, NY, New York - 175,100.00 - 236,900.00 USD annually
USA, TX, Austin - 159,200.00 - 215,300.00 USD annually
USA, TX, Dallas - 159,200.00 - 215,300.00 USD annually
USA, VA, Arlington - 159,200.00 - 215,300.00 USD annually
USA, WA, Seattle - 159,200.00 - 215,300.00 USD annually