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About This Role
DESCRIPTION
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Ring \& Blink Customer Service Technology is looking for a Solutions Architect to design and deliver AI\-powered customer service systems at scale. This role sits at the intersection of Amazon Connect, Salesforce, and large language model (LLM) services, architecting the intelligent layer that powers voice AI, chat automation, and self\-service experiences for millions of Ring and Blink customers.
You will own the end\-to\-end technical architecture for our contact center AI stack: designing LLM integration patterns with Amazon Bedrock, building AI agent orchestration across voice and chat channels, and driving the convergence of our Salesforce CRM and Amazon Connect platforms into a unified, AI\-native system. This is not a traditional infrastructure role. You are the person who translates business problems into AI\-powered solutions, validates them with data, and ships them into production.
We are a team that builds fast, measures everything, and treats AI as a core capability rather than an experiment. You will work directly with CS Operations leadership, product managers, and engineering to design systems that increase automation, reduce cost per contact, and improve the customer experience.
Key job responsibilities
- Design and implement AI\-powered contact flow architectures using Amazon Connect, Amazon Bedrock, and Salesforce AI services (AgentForce, Einstein)
- Own the technical architecture for voice AI systems: IVR\-to\-LLM handoff patterns, intent classification, real\-time transcription pipelines, and containment optimization
- Architect RAG (Retrieval Augmented Generation) implementations that connect knowledge bases to AI agents for accurate, context\-aware customer interactions
- Lead the Salesforce/Connect convergence architecture, ensuring AI capabilities are natively integrated rather than bolted on
- Design prompt engineering frameworks and evaluation systems that measure AI output quality, hallucination rates, and customer satisfaction impact
- Build AI agent orchestration patterns across channels (voice, chat, self\-service portals) with consistent fallback and escalation logic
- Create technical design documents, integration specifications, and architecture diagrams that engineering teams can execute against
- Partner with AWS service teams on Connect AI features (Connect Assistant, Agent Assist, Q in Connect) to evaluate, pilot, and integrate new capabilities
- Develop A/B testing frameworks for AI features, measuring containment rate, resolution rate, CSAT, and cost per contact
- Collaborate with CS Operations, Workforce Management, and Quality Assurance to ensure AI solutions meet operational requirements and scale targets
A day in the life
You start your day joining a working session with the engineering team to finalize the integration design for a new Bedrock\-powered summarization service that will give agents real\-time context from prior customer contacts. You are mapping the data flow, defining the API contracts, and mapping interdependencies.
After lunch, you are on a call with the AWS Connect team reviewing a new AI feature in preview. You assess whether it fits your architecture, what the integration lift looks like, and whether it solves a real operational pain point. You document your recommendation and share it with the product manager.
This role requires someone who is equally comfortable in a technical design review and a business strategy discussion with operations stakeholders. You are the connective tissue between what AI can do technically and what CS Operations needs it to do in practice.
About the team
Ring \& Blink CS Technology Enablement owns the systems that power customer service for Ring, Blink, and Key by Amazon. We operate at the intersection of AI, contact center technology, and operational excellence.
We are a team of builders who ship fast and iterate weekly. AI is not a side project here. It is the core of our strategy: automate what we can, empower associates with AI for everything else, and measure the impact of both. You will join a team that has already deployed production voice AI, built LLM\-powered agent assist tools, and is actively expanding AI capabilities across every customer touchpoint.BASIC QUALIFICATIONS
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- 2\+ years of design, implementation, or consulting in applications and infrastructures experience
- 4\+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data \& analytics) experience
- 5\+ years of IT development or implementation/consulting in the software or Internet industries experience
PREFERRED QUALIFICATIONS
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- Experience working within software development or Internet\-related industries
- Experience migrating or transforming legacy customer solutions to the cloud
- Experience working with AWS technologies from a dev/ops perspective
- 4\+ years of customer service experience, or experience in technical support
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, Hawthorne \- 131,300\.00 \- 177,600\.00 USD annually
Salary Context
This $131K-$177K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Amazon.com, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($154K) sits 15% below the category median. Disclosed range: $131K to $177K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Amazon.com AI Hiring
Amazon.com has 102 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Product Manager, Data Scientist. Positions span New York, NY, US, Palo Alto, CA, US, Bellevue, WA, US. Compensation range: $129K - $300K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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