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About This Role
DESCRIPTION
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Are you ready to lead the world's largest organizations through application modernization? Are you passionate about driving meaningful business changes with Agentic AI? The Agentic Development \& Developing Agents (ADDA) organization is responsible for building tools and services that enable AWS customers to leverage Agentic AI to transform and modernize how they build applications on AWS with services such as Kiro. Please see https://kiro.dev/ for more details. We move quickly, experiment, and deliver new capabilities into the hands of our customers at a rapid pace. Join the team as a Sr. Technical Business Development Specialist, Agentic AI for Application Modernization
The Worldwide Specialist Organization (WWSO) is part of AWS Sales, Marketing, and Global Services (SMGS), which is responsible for driving revenue, adoption, and growth from the largest and fastest growing small\- and mid\-market accounts to enterprise\-level customers including public sector. We work 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. WWSO teams include business development, specialist and technical solutions architecture. As part of WWSO, you'll provide expertise across the entire life cycle of an AWS customer initiative, from developing ideas for new services to accelerating the adoption of established businesses. We pride ourselves on thinking big, delivering exceptional results for our customers, and working across AWS as \#OneTeam.
Key job responsibilities
Within WWSO, this position is part of the ADDA Specialist Team, where you will lead the enterprise application modernization with Agentic AI services. You will lead the the effort in driving the development and adoption of AWS' agentic AI services through deep customer engagements. You are a leader for specialists, architects, partners, and account teams and set direction and standards for how we solve customer problems in your domain. You partner with technical and business teams across AWS and bring the voice of the customer into our product development roadmap. This team sees the big picture and sets the plan so the world’s most customer obsessed technical teams can make history.
Ideally, you’re someone who has background in enterprise applications, modernization with Agentic AI and traditional tools and content for developers, and has an unmet need to seed developer communities and programs around a technology you heartily believe in. You have immediate credibility with application architects and developers at all levels and able to earn trust with CTOs and CIOs. You love to share your passion with others and exhibit good judgment in selecting strategic opportunities to do so. You don’t just want to be part of an industry movement, you want to be out front leading it. If this sounds like you, this is your dream job.
A day in the life
- Serve as the opportunity owner and primary point of contact for a portfolio of strategic customer accounts, managing relationships and acting as a strategic advisor for application modernization with Agentic AI services.
- Deliver compelling presentations, product demos, sample solutions and programs, events (such as Workshops, Hackathons) to drive fast modernization with Agentic AI.
- Build and engage with C\-level stakeholders to understand the value prop of modernization with AI.
- Bring market signals back to product teams to drive innovation on AWS product roadmaps.
- Own reporting and planning cadences to AWS executives on application modernization execution.
- Demonstrate thought leadership and be able to credibly represent AWS at industry events, conferences, symposiums, etc.
About the team
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 including AI 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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- 6\+ years of working with Enterprise Application Modernization and Migration technologies, including, but not limited to, Mainframe, Serverless, Containers, or Cloud Operations experience
- 5\+ years of working with Data \& AI related technologies, including, but not limited to, AI/ML (Artificial Intelligence/Machine Learning), GenAI (Generative AI), Analytics, Database, and/or Storage experience
- 5\+ years of Go\-To\-Market, Business Development, Sales, or Consulting experience
- 5\+ years of working with Core Cloud Technology Services, including, but not limited to Compute, Edge, Hybrid, Security, and/or Networking experience
- Experience developing strategies that influence leadership decisions at the organizational level
- Experience selling enterprise software or cloud\-based applications
- Experience explaining complex technical concepts to various business and technical audiences
- Experience presenting to both technical and non\-technical executive audiences
- Experience leading complex, multi\-year initiatives that may be cross\-functional and/or span business and technology
- Experience managing programs across cross functional teams, building processes and coordinating release schedules
PREFERRED QUALIFICATIONS
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- Experience interpreting data and making business recommendations across leadership and cross\-functional teams
- Experience identifying, negotiating, and executing complex legal agreements
- Experience using analytical tools for workforce metrics and reporting
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, NY, New York \- 162,700\.00 \- 220,200\.00 USD annually
USA, TX, Austin \- 147,900\.00 \- 200,100\.00 USD annually
USA, VA, Arlington \- 147,900\.00 \- 200,100\.00 USD annually
USA, VA, Herndon \- 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. Senior-level AI roles across all categories have a median of $227,400. 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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