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About This Role
DESCRIPTION
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This position is part of the AWS Specialist and Partner Organization (ASP), where Specialists own the end\-to\-end go\-to\-market strategy for their respective technology domains, providing the business and technical expertise to help our customers succeed. Together, they provide customers with the expertise and scale needed to build innovative solutions for their most complex challenges.
The Business Development Specialist, AI Acceleration – Amazon Connect is responsible for driving the adoption of Amazon Connect's generative and agentic AI capabilities across global markets. This role leads the development and execution of AI\-focused go\-to\-market strategies and orchestrates cross\-functional alignment between AWS field teams and internal stakeholders to accelerate customer adoption and revenue growth.
Key job responsibilities
- Develop data\-driven GTM strategies and frameworks that identify customer segments, market opportunities, and AI adoption pathways specific to Amazon Connect's generative and agentic AI capabilities
- Create and execute scalable AI\-focused GTM campaigns with measurable goals, timelines, and success metrics — including sales plays, field enablement programs, and industry\-specific use cases tied to Connect's 2026 growth objectives
- Drive alignment across diverse stakeholders including sales executives, product teams, and marketing to ensure coordinated execution of Connect's AI go\-to\-market strategy across global markets
- Build strategic relationships with C\-suite executives and business leaders to position Amazon Connect's AI capabilities as a transformational solution for enterprise customer experience modernization
- Analyze market dynamics, competitive positioning (including emerging agentic AI entrants and hyperscalers), and customer needs to inform strategic recommendations and multi\-year planning for Connect
- Lead cross\-functional initiatives that improve Amazon Connect's GTM capabilities, operational excellence, and business rhythms — including field enablement, pipeline development, and adoption acceleration programs
A day in the life
You'll analyze Amazon Connect adoption data and market trends to identify growth opportunities, then translate insights into actionable GTM strategies. Your day involves collaborating with internal stakeholders—sales leaders, product teams, marketing, partner organizations, and enablement—to align on campaigns, messaging, and field execution. You'll engage directly with customers—enterprise C\-suite executives and contact center leaders—to understand their customer experience transformation needs and position Connect's generative and agentic AI capabilities as the solution.
Problems you'll solve:
For customers: Articulating the ROI of cloud contact center migration and AI\-powered customer service in an increasingly competitive CX landscape
For field teams: Removing sales friction through playbooks, tools, and competitive positioning against emerging agentic AI entrants and hyperscalers
For the business: Accelerating revenue growth through repeatable, scalable GTM motions across enterprise target segments
About the team
The AI Acceleration Business Development team for Amazon Connect is a dynamic, high\-impact group that sits at the intersection of strategy, sales, and AI innovation. Our mission is to accelerate Connect's growth by developing data\-driven go\-to\-market strategies that drive adoption of Connect's generative and agentic AI capabilities — unlocking new market opportunities and enabling our field teams to win in an AI\-first CX market.
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred 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
AWS values curiosity and connection. Our employee\-led and company\-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.
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.BASIC QUALIFICATIONS
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- 5\+ years of working with Core Cloud Technology Services, including, but not limited to Compute, Edge, Hybrid, Security, and/or Networking experience
- 5\+ years of working with Business Application Technologies, including, but not limited to End User Compute (EUC), Supply Chain, Contact Center as a Service, Consumer Data Applications, Encrypted Communications, and/or Communication Developer Services experience
- 5\+ years of Go\-To\-Market, Business Development, Sales, or Consulting experience
- Bachelor's degree
- Experience developing strategies that influence leadership decisions at the organizational level
PREFERRED QUALIFICATIONS
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- Experience interpreting data and making business recommendations
- MBA
- Experience selling cloud solutions at a software company or equivalent
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, IL, Chicago \- 147,900\.00 \- 200,100\.00 USD annually
USA, MA, Boston \- 147,900\.00 \- 200,100\.00 USD annually
USA, NY, New York \- 162,700\.00 \- 220,200\.00 USD annually
USA, WA, Seattle \- 147,900\.00 \- 200,100\.00 USD annually
Salary Context
This $147K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1889 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 3,736 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 $181,357 based on 12,694 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $147K to $200K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,650. 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: $248,100; VP: $250,000.
Amazon Web Services AI Hiring
Amazon Web Services has 78 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Product Manager, Research Scientist. Positions span Seattle, WA, US, Arlington, VA, US, San Francisco, CA, US. Compensation range: $177K - $295K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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 3,736 open positions tracked in our dataset. By seniority: 109 entry-level, 1,755 mid-level, 1,486 senior, and 386 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (562 positions). The remaining 3,158 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,650. 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 3,736 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,564), Data Scientist (311), AI Software Engineer (277). 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 (109) are outnumbered by mid-level (1,755) and senior (1,486) 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 386 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (562 positions), with 3,158 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 $253,650, 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 (1,942 postings), Aws (1,175 postings), Azure (881 postings), Rag (827 postings), Gcp (718 postings), Prompt Engineering (590 postings), Pytorch (586 postings), Claude (528 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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