Amazon Web Services (AWS)
Sr. Applied AI Solutions Architect, Amazon Connect

How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Sr. Applied AI Solutions Architect, Amazon Connect
Description
Are you a customer-obsessed builder with a passion for helping customers achieve their full potential? Do you have the technical background, customer experience, and skills necessary to help accelerate customer adoption of Amazon Connect's AI capabilities? Do you love building new strategic and data-driven businesses? Join the Applied AI Solutions team as an Amazon Connect Specialist Solutions Architect!
The Applied AI Solutions Architecture team is seeking a hands-on, customer-obsessed Solutions Architect to accelerate customer adoption of Amazon Connect's AI capabilities. Applied AI Solutions is part of the AWS Specialist & Partner (ASP) org, which works backwards from our customer’s most complex and business critical problems to build and execute go-to-market plans that turn AWS ideas into multi-billion-dollar businesses. We pride ourselves on thinking big, delivering exceptional results for our customers, and working across AWS as #OneTeam.
A critical dimension of this role is Customer Data Readiness — assessing, preparing, and structuring customer data assets so that AI agents can reliably access, retrieve, and act on the right information. You will help customers evaluate their data landscape, identify gaps, establish data pipelines, and ensure their knowledge bases, CRMs, and backend systems are AI-ready before agents go live. You will work at the intersection of contact center operations and applied AI, helping customers move from proof-of-concept to pre-production for their Amazon Connect deployments. We stay closely connected to our customers and bring valuable data and insights to our product teams, strengthening the product roadmap. Our team is at its best when a customer is thinking big and needs specialized experience to innovate for their business.
Key job responsibilities
Customer Engagement: Lead technical discovery sessions with customer teams to understand business requirements, existing contact center architecture, and AI readiness. Translate findings into actionable implementation plans. Customer Data Readiness: Conduct data readiness assessments to evaluate the quality, accessibility, structure, and governance of customer data assets (CRMs, knowledge bases, ticketing systems, order management, etc.). Identify data gaps, recommend remediation strategies, and help customers build the data foundation required for effective AI agent tool use and RAG-powered responses. Agentic AI Implementation: Design and configure agentic AI solutions within Amazon Connect, including AI agent creation, AI prompt engineering, model selection, guardrail configuration, and tool/action integration. A2A (Agent-to-Agent) Integration: Architect Agent-to-Agent communication patterns that allow Amazon Connect AI agents to collaborate with specialized agents across the enterprise (e.g., billing agents, order management agents, IT support agents), enabling multi-agent workflows that span organizational boundaries. Integration Development: Build serverless integrations using AWS Lambda, API Gateway, Step Functions, and scripting (Python, Node.js) to connect Amazon Connect AI agents with customer data systems (CRMs, ERPs, databases, knowledge bases). Cloud Data Access: Architect secure access patterns to cloud-based data systems to power AI agent tool use and retrieval-augmented generation (RAG). Agentic IDE Proficiency: Leverage agentic development environments such as Kiro (and similar AI-assisted IDEs) to accelerate development workflows, including spec-driven development, agent hooks, MCP server configuration, and AI-assisted code generation. Pre-Production Validation: Guide customers through testing, evaluation, and validation of AI agent performance against defined success criteria before production deployment. Field Enablement: Share learnings, delivering technical deep-dives, and mentoring other SAs on agentic AI implementation patterns.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
A day in the life
Conducting data readiness assessments, identifying gaps in knowledge base coverage, and recommending data preparation steps before AI agent configuration Designing prompt strategies and evaluating model performance across different foundation models Building Lambda functions and API integrations that serve as tools for AI agents Configuring MCP servers to expose customer APIs, databases, and tools in a standardized format for agent consumption Designing A2A workflows where Amazon Connect agents hand off to or collaborate with specialized agents across the customer's enterprise Configuring knowledge bases and data connectors for RAG-powered agent responses Running evaluation frameworks to measure AI agent accuracy, latency, and customer satisfaction Conducting architecture reviews and providing prescriptive guidance for production readiness Documenting implementation patterns and contributing to the team's knowledge base
About The Team
The Applied AI Solutions Architecture team is part of the AWS Specialist and Partner Organization (ASP). We are the technical bridge between Amazon Connect customers and the service teams building the next generation of AI-powered contact center capabilities. Our team operates at the forefront of agentic AI adoption, helping customers become production-ready with Amazon Connect's Unlimited AI features.


Get help with your application
Your very own career expert that helps elevate your application to the next level.
Basic Qualifications
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
Experience working with and presenting to C-level executives, IT, and lines of businesses across organizations or equivalent AWS certification, such as, AWS Solutions Architect, or a similar cloud certification Knowledge of data structures, data modeling, and database schema Experience architecting, migrating, transforming or modernizing customer requirements to the cloud Experience with Amazon Connect or other enterprise contact center platforms (Genesys, Avaya, Cisco, NICE, Five9, etc.) Hands-on experience with Amazon Bedrock, including model invocation, agent creation, knowledge base configuration, and guardrails
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.
Company - AWS EMEA SARL (UK Branch)
Job ID: A10393427
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location