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Senior Delivery Consultant AI - Enterprises, AWS Professional Services

Amazon

Senior Delivery Consultant AI - Enterprises, AWS Professional Services

full-timePosted: Aug 19, 2026Updated: Aug 27, 2026London, England, United Kingdom

Job Description

The Amazon Web Services Professional Services (ProServe) team is seeking a Senior Delivery Consultant specialising in AI/ML and generative AI to join our Data & AI practice. In this role, you will lead the technical direction and delivery of complex enterprise AI engagements, working across multiple workstreams and helping customers establish repeatable capabilities for adopting AI responsibly at scale. This is a senior, hands-on individual-contributor role. You will move between strategic and implementation detail: shaping solutions and delivery roadmaps around customer use cases with senior stakeholders, making architecture and engineering trade-offs, reviewing code and evaluation evidence, and removing technical blockers for delivery teams. You will operate effectively when requirements are incomplete, making evidence-based recommendations and creating clarity for customer, AWS, and partner teams. Beyond individual solutions, you will help customers address the wider conditions required for sustained value from AI, including data readiness, platform architecture, security, governance, operating models, adoption, and value measurement. You will also strengthen the Data & AI practice through mentoring, technical standards, reusable assets, and feedback from customer delivery. The AWS Professional Services organisation is a global team of experts that helps customers realise their desired business outcomes using AWS. We work with customer teams and the AWS Partner Network (APN) to execute enterprise cloud and AI transformation initiatives. Key job responsibilities - Translate customer use cases and business objectives into well-scoped AI solutions with measurable success criteria, technical requirements, and an iterative delivery plan. - Design and implement production AI solutions on AWS, including the application, data, integration, security, and operational components required to run them reliably. - Develop and test software using one or more programming languages, APIs, data pipelines, infrastructure as code, and continuous delivery practices. - Select and evaluate appropriate AI approaches, which may include traditional ML, foundation models, retrieval, model adaptation, tool use, or agentic workflows. - Establish evaluation and responsible AI controls covering solution quality, safety, privacy, security, latency, reliability, and cost. - Diagnose technical issues across models, applications, data, and infrastructure, and communicate trade-offs clearly to customer stakeholders. - Work with customer and partner teams to integrate solutions into existing workflows and complete documentation, knowledge transfer, and operational handover. - Use AI-assisted development practices, and contribute reusable assets and lessons to the Data & AI practice. A day in the life A typical day may include working with a customer team to turn an agreed use case into a detailed solution design and delivery backlog. You might review evaluation results from an AI application, investigate a quality, security, latency, or cost issue, and implement and test an improvement. Later, you may run a technical workshop to resolve data or integration constraints, pair with a customer engineer on code, or review an architecture decision with security and platform teams. You will regularly communicate progress, risks, and technical trade-offs to business and technical stakeholders. As delivery moves towards production, you will help establish monitoring and operational processes, complete documentation and knowledge transfer, and ensure the customer team can operate and extend the solution after the engagement. About the team Amazon values diverse experiences and encourages candidates to apply even if they do not meet every preferred qualification. AWS teams bring together people with varied backgrounds and perspectives to help customers design, build, operate, and secure cloud environments. Employee-led affinity groups, inclusion events, knowledge sharing, and mentorship support an inclusive culture and continued professional growth. We also value work-life harmony and strive for flexibility as part of our working culture. You will join technical experts across AWS Professional Services who work with customers and AWS Partners to achieve business outcomes using cloud and AI technologies.

Locations

  • London, England, United Kingdom

Skills Required

  • technical leadership of developmentintermediate
  • generative AIintermediate
  • cloud-native architectureintermediate
  • AWS services including S3intermediate
  • machine learningintermediate

Required Qualifications

  • Experience as technical specialist in design and architecture (experience)
  • Experience developing, deploying and managing AI products at scale (experience)
  • Experience in technical leadership of development, testing, and implementation of large-scale, complex technology projects (experience)
  • - Hands-on experience with generative AI or machine learning systems, including architecture, implementation, evaluation, and production operation. (experience)
  • - Experience with cloud-native architecture and software delivery practices, including APIs, data platforms, infrastructure as code, testing, CI/CD, security, and observability. (experience)
  • - Experience influencing senior business and technical stakeholders and translating strategic objectives into executable technical plans. (experience)

Preferred Qualifications

  • Ph.D. in computer science, machine learning, engineering, or related fields, or experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2 (experience)
  • Experience building complex software systems that have been successfully delivered to customers, or experience in machine learning, data mining, information retrieval, statistics or natural language processing (experience)
  • Experience working with or evaluating AI systems (experience)
  • Experience mentoring or training the engineering community on complex technical issues (experience)
  • - Experience designing enterprise data or knowledge architectures that support multiple AI use cases. (experience)
  • - Experience developing technical standards, delivery methods, offerings, or reusable accelerators adopted by other teams. (experience)
  • - Relevant AWS certification (e.g Generative AI Developer Professional). (certification)
  • 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. (experience)

Responsibilities

  • Translate customer use cases and business objectives into well-scoped AI solutions with measurable success criteria, technical requirements, and an iterative delivery plan.
  • Design and implement production AI solutions on AWS, including the application, data, integration, security, and operational components required to run them reliably.
  • Develop and test software using one or more programming languages, APIs, data pipelines, infrastructure as code, and continuous delivery practices.
  • Select and evaluate appropriate AI approaches, which may include traditional ML, foundation models, retrieval, model adaptation, tool use, or agentic workflows.
  • Establish evaluation and responsible AI controls covering solution quality, safety, privacy, security, latency, reliability, and cost.
  • Diagnose technical issues across models, applications, data, and infrastructure, and communicate trade-offs clearly to customer stakeholders.
  • Work with customer and partner teams to integrate solutions into existing workflows and complete documentation, knowledge transfer, and operational handover.
  • Use AI-assisted development practices, and contribute reusable assets and lessons to the Data & AI practice.

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