At a Glance
- Tasks: Lead AI projects, design solutions, and mentor teams in a dynamic environment.
- Company: Join Amazon Web Services, a leader in cloud technology and innovation.
- Benefits: Enjoy flexible work-life balance, competitive salary, and professional growth opportunities.
- Other info: Diverse and inclusive culture that values your unique experiences.
- Why this job: Make a real impact by shaping the future of AI in enterprise solutions.
- Qualifications: Experience in AI/ML, technical leadership, and cloud-native architecture required.
The predicted salary is between 72000 - 88000 £ per year.
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 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.
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.
Basic qualifications
- Experience as technical specialist in design and architecture.
- Experience developing, deploying and managing AI products at scale.
- Experience in technical leadership of development, testing, and implementation of large-scale, complex technology projects.
- Hands-on experience with generative AI or machine learning systems, including architecture, implementation, evaluation, and production operation.
- Experience with cloud-native architecture and software delivery practices, including APIs, data platforms, infrastructure as code, testing, CI/CD, security, and observability.
- Experience influencing senior business and technical stakeholders and translating strategic objectives into executable technical plans.
Preferred qualification
- 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 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 working with or evaluating AI systems.
- Experience mentoring or training the engineering community on complex technical issues.
- Experience designing enterprise data or knowledge architectures that support multiple AI use cases.
- Experience developing technical standards, delivery methods, offerings, or reusable accelerators adopted by other teams.
- Relevant AWS certification (e.g Generative AI Developer Professional).
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.
Senior Delivery Consultant AI - Enterprises, AWS Professional Services in London employer: AmazonWebServices
Amazon Web Services (AWS) is an exceptional employer, offering a dynamic work environment where innovation thrives. As part of the UK Solutions Architecture team, you will engage with diverse clients in the Betting & Gaming industry, leveraging cutting-edge cloud technologies to drive meaningful change. With a strong focus on employee growth, AWS provides ample opportunities for professional development and encourages a culture of collaboration and inclusivity, making it a rewarding place to build your career.
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We think this is how you could land Senior Delivery Consultant AI - Enterprises, AWS Professional Services in London
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We think you need these skills to ace Senior Delivery Consultant AI - Enterprises, AWS Professional Services in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at AmazonWebServices.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at AmazonWebServices and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at AmazonWebServices
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If AmazonWebServices uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
