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About This Role
DESCRIPTION
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Amazon Web Services (AWS) is seeking an experienced and deeply technical Generative AI Solutions Architect to accelerate AI adoption across North America's largest telecommunications operators. This is a builder\-first role. You will prototype, architect, and deliver production\-grade generative AI solutions spanning agentic voice systems, AI\-powered SMB assistants, personal subscriber assistants, real\-time translation, and intelligent network operations.
In this highly technical and customer\-facing role, you will lead the design and implementation of AI\-powered experiences that transform how telecommunications companies serve their customers and run their networks. You will bridge the gap between frontier AI capabilities (large language models, multimodal foundation models, retrieval\-augmented generation, speech\-to\-speech models) and real\-world telco environments, from voice infrastructure (SBCs, IMS, SIP) to subscriber platforms and network operations centers.
As a trusted technical advisor, you will guide telco engineering and product teams through designing AI\-native products and services: agentic voice pipelines for customer care, AI assistants that automate appointment booking and call answering for SMB customers, multilingual real\-time translation, and GenAI copilots for network operations. You will work backwards from customer outcomes to define reference architectures that combine AWS AI services (Amazon Bedrock, Amazon Transcribe, Amazon Polly, Amazon Connect) with partner and open\-source components in carrier\-grade deployments.
This role requires someone who builds. You will write code, construct proof\-of\-concept implementations, contribute to open\-source projects, and develop reusable blueprints that accelerate GenAI adoption across the telco vertical. You will influence AWS product roadmaps by translating customer requirements into feature requests and working directly with service teams. You will publish your work through whitepapers, blog posts, reference architectures, and conference presentations.
The ideal candidate combines deep generative AI expertise with a passion for shipping AI products in complex, regulated environments. You are equally comfortable whiteboarding transformer architectures with ML engineers, designing multi\-turn agentic workflows, and understanding the operational realities of carrier\-grade telecommunications networks.
Key job responsibilities
- Design and deliver generative AI solutions for telco customers across multiple domains: agentic voice, AI assistants, real\-time translation, and network operations AI.
- Architect end\-to\-end AI pipelines integrating foundation models, speech, vision, and tool\-use agents with telco platforms and infrastructure.
- Build production\-quality proof\-of\-concepts demonstrating agentic capabilities including multi\-turn reasoning, tool use, RAG, and real\-time voice interaction.
- Lead technical engagements with telco engineering teams on foundation model selection, fine\-tuning, prompt engineering, guardrails, and responsible AI.
- Provide strategic guidance on deploying AI agents into telco environments, addressing latency, reliability, compliance, and scale requirements.
- Develop reference architectures and reusable blueprints that accelerate GenAI adoption, combining Bedrock, Transcribe, Polly, Connect, and partner ecosystem components.
- Influence AWS AI service roadmaps by capturing customer requirements and collaborating with service teams.
- Author whitepapers, blog posts, and reference implementations establishing AWS's point of view on GenAI for telecommunications.
- Drive customer growth through GenAI\-focused sales strategies in partnership with AWS Sales teams.
- Build trusted advisor relationships with senior technical leaders (VP Eng, CTO, Chief AI Officer) at telco customers.
A day in the life
You whiteboard an agentic voice architecture with a Tier 1 operator's engineering team, then shift to coding a proof\-of\-concept for an AI\-powered assistant that handles appointment scheduling and call answering. In the afternoon, you join a Bedrock product roadmap session advocating for features your customers need, then review a partner's proposal for integrating real\-time translation into a subscriber app. Every engagement produces a tangible artifact (code, architecture, or content) that scales beyond a single customer.
About the team
The AWS Industries Telco SA team partners with North America's largest telecommunications companies to accelerate cloud transformation and AI adoption. We combine deep telco industry expertise with technical depth across AI/ML, networking, and modern application architectures. We operate at the intersection of frontier AI and carrier\-grade operational requirements, helping customers reinvent their customer experiences through generative AI. We are builders who lead by example: we publish, code, prototype, and scale solutions across the industry.
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee\-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship \& Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge\-sharing, mentorship and other career\-advancing resources here to help you develop into a better\-rounded professional.
Work/Life Balance
We value work\-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.BASIC QUALIFICATIONS
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- 10\+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data \& analytics) experience
- Experience communicating across technical and non\-technical audiences and at C\-level, including training, workshops, publications
- Experience developing technology solutions and evangelising end\-to\-end technology roadmaps that guide IT transformations toward cloud computing
- Experience building complex software systems that have been successfully delivered to customers, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
- Experience designing, building, operating, and managing large\-scale distributed systems or web services
PREFERRED QUALIFICATIONS
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- Experience with voice interface, natural language or multimodal design
- Experience writing technical articles, speaking at technology conferences, and contributing to open source
- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience programming with at least one modern language such as Java, C\+\+, or C\# including object\-oriented design
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.
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, WA, Bellevue \- 182,800\.00 \- 247,300\.00 USD annually
Salary Context
This $182K-$247K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Amazon Web Services, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($215K) sits 16% above the category median. Disclosed range: $182K to $247K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Amazon Web Services AI Hiring
Amazon Web Services has 80 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Agent Developer, Data Scientist. Positions span New York, NY, US, Seattle, WA, US, Bellevue, WA, US. Compensation range: $177K - $299K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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