Senior GenAI/ML Specialist SA, UKI-AWS Global Sales (AGS)

Amazon is hiring a Senior GenAI/ML Specialist SA, UKI-AWS Global Sales (AGS) in London, United Kingdom. Level rates it ; you can apply on Amazon.

Level's read on this job

Works on AI ●●●○ is how Level rates this sales role: the daily work is on AI products, without building the model.
Tools and skills the posting cites include Hugging Face, Bedrock and PyTorch.
Level classifies it as a senior role.
Level lists 20,000+ other open roles at Amazon.

AI in this role

Drive enterprise adoption of AWS GenAI and Machine Learning services through expert architectural design and advisory.

Hugging FaceBedrockPyTorchTensorFlowscikit-learnSageMakerAWSGenerative AIMachine learning

prompt-engineeringragfine-tuningml-opscomputer-visionnlpsolution-architectureenterprise-architecturetechnical-leadershipconsulting

AWS is seeking a Senior GenAI/ML Specialist Solutions Architect (SSA) for large enterprise customers in United Kingdom of Great Britain and Ireland (UKI). You'll be instrumental in driving the adoption of AWS GenAI/ML services while helping organizations achieve their innovation and transformation goals through GenAI/ML architectures.

This role requires technologists with deep domain-specific expertise, able to address advanced concepts and feature designs. As part of the AGS organization, SSAs work with customers who have complex requirements that require expert-level knowledge to solve. You must have deep technical expertise in Generative AI and Machine Learning services, enterprise-grade architectural strategies, and a passion for solving complex business challenges. We are looking for technical experts who can bridge the gap between innovative AI technologies and practical business outcomes for our customers.

You'll work directly with a diverse top range of customers in UKI to help them make optimal technical decisions in their AI/ML journey, balancing innovation with practical constraints and compliance requirements. You'll have the opportunity to build enduring relationships with these organizations and establish yourself as a trusted technical advisor in the rapidly evolving field of Generative AI and Machine Learning.

Key job responsibilities
1. Technical Expertise & Solution Design: Provide customers with deep technical expertise in Generative AI and Machine Learning technologies, designing innovative solutions that help them meet strategic business objectives using AWS products

2. Customer Advisory: Act as a trusted advisor to line of business, AI, Data, and C-suite leaders, translating complex technical concepts into business value and guiding their AI transformation journey

3. Technical Leadership: Lead architectural reviews, workshops, and proof-of-concepts to advance customers' technical objectives and validate proposed solutions

4. Thought Leadership: Share best practices through AWS blogs, whitepapers, reference architectures, and helping to educate the broader technical community

5. Product Feedback & Innovation: Gather customer insights to inform AWS product teams about market needs and opportunities, contributing to the evolution of AWS GenAI/ML services based on real-world implementation experience

6. External Representation: Serve as a thought leader in the Generative AI space, representing AWS at industry events and conferences, such as AWS Summit and re:Invent

A day in the life
You bring hands-on experience designing and implementing machine learning solutions, including model training, deployment, and inference at scale, with working knowledge of AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, and scikit-learn. You have a background in a customer-facing technical role (solutions architect, ML engineer, data scientist, or technical consultant) and can communicate complex technical concepts to both technical and non-technical audiences, including C-suite executives. You hold domain expertise in one or more of: natural language processing, computer vision, generative AI, recommendation systems, or conversational AI.

Ideally, you also have experience with large language models (LLMs), foundation models, retrieval-augmented generation (RAG), fine-tuning, prompt engineering, or agentic AI architectures. Hands-on experience with AWS AI/ML services such as Amazon Bedrock, Amazon SageMaker, Amazon Q, or Amazon Rekognition is highly valued. Experience architecting end-to-end ML pipelines including data ingestion, feature engineering, model training, evaluation, and production deployment (MLOps) would further strengthen your candidacy.

Basic qualifications

- Bachelor's degree in computer science, engineering, mathematics or equivalent, or experience in a professional field or military
- Experience in IT development or implementation/consulting in the software or Internet industries
- Experience within specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics).
- Experience in design, implementation, or consulting in applications and infrastructures
- Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients

Preferred qualifications

- Knowledge of SAP systems (like SAP Business Suite, S/4HANA, SAP Business Warehouse, SAP HANA, SAP Business Objects, etc.) and their architecture and infrastructure needs
- Experience in infrastructure architecture, database architecture and networking
- Experience working with end user or developer communities

Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

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.

How we rate this

Senior GenAI/ML Specialist SA, UKI-AWS Global Sales (AGS) at Amazon rates 75 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior GenAI/ML Specialist SA, UKI-AWS Global Sales (AGS) is an open job at Amazon.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in GB, London.
The job was posted on September 1, 2026.
Seniority: senior.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Prompt engineeringRAGFine-tuningML OpsComputer visionNLPSolution ArchitectureEnterprise Architecture

Also common in sales jobs at this level

AI agents Azure Google Cloud Python Databricks Kubernetes

Pay

Typical sales job: $140k

This posting doesn't list pay. Use the typical sales figure when pay comes up. See sales jobs

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
Walk me through fine-tuning a model: what data did you use, and how did you check the result?
How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through a computer vision problem you solved, from raw data to a deployed model.

Adapt your resume

List these exact terms on your resume: Prompt engineering, RAG, Fine-tuning, ML Ops, and Computer vision. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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