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About This Role
DESCRIPTION
We're looking for a Consultant who will become the connective tissue between our global agentic delivery programs and the regional teams that bring them to life. You'll own the relationship with 1-2 geographic regions - ensuring that innovations, tooling, and frameworks don't just get built centrally, but actually land with the practitioners who need them most.
What makes this role different: you're not building the tools or writing the training. You're the person who makes sure the right people in the right geos know what's available, can access it, and have a direct line back to influence what gets built next. You'll be equal parts relationship builder, orchestrator, and feedback channel - the trusted face that thousands of delivery practitioners look to for what's new and what's coming.
Bridge global strategy with regional execution across AWS Professional Services
Build deep relationships with geographic and delivery leaders across assigned regions
Channel critical field feedback back to shape product and program direction
Orchestrate cross-functional programs so your regions get the full benefit of our portfolio
Key job responsibilities
Key job responsibilities
Own end-to-end relationships with geographic leaders, delivery leaders, and key stakeholders across 1-2 assigned regions, serving as their primary point of contact for all delivery acceleration initiatives
Cascade programs, frameworks, and best practices into your regions with clarity and context - translating global strategy into language and actions that resonate locally
Collect and channel field feedback, adoption blockers, and innovation signals back to inform product direction and program iteration
Orchestrate cross-functional coordination so your regions receive enablement, champion network support, user acceptance testing, and technical intake services
Partner with our enablement organization to identify knowledge gaps in the field and develop training content that meets real practitioner needs
A day in the life
You'll start your day reviewing field feedback from your assigned regions - spotting adoption blockers and opportunities that need attention. You might run a working session with delivery leaders to contextualize new tooling for their teams, then coordinate with enablement to turn those insights into training. Throughout the week, you'll maintain regular touchpoints with your regional stakeholders, route incoming requests, support field champions evangelizing innovations locally, and recruit practitioners for testing programs. Your work spans time zones and cultures - you're the person who keeps global and regional in sync.
About the team
We're driving ProServe's transformation toward agentic automation and delivery productivity - and our customer is the field. ADAPT builds and scales the tools, frameworks, and guidance that help thousands of practitioners work in fundamentally new ways. This team serves as the bridge between what gets built globally and how it lands regionally - making sure innovations generate real value across all six geographies. You'll work alongside program leaders, enablement specialists, and practitioners who are obsessed with making the field more productive.
BASIC QUALIFICATIONS
- 5+ years of leading the delivery of IT projects with external customers or equivalent experience
- 5+ years of program or engagement management work leading other project managers to deliver a program with multiple and concurrent projects experience
- 1+ years of working as a Scrum Master or Product Owner across multiple cross-functional teams experience
- 3+ years of cloud computing experience
- Bachelor's degree in Computer Science or Engineering
- Experience working as a Scrum Master or Product Owner across multiple cross-functional teams
- Experience in leading the delivery of IT projects with external customers or equivalent
- Experience in program or engagement management, leading other project managers to deliver a program with multiple and concurrent projects
- Experience with Agile and project management toolsets like Jira, Rally, VersionOne or Taiga
- Bachelor's degree or equivalent
- Experience in project management of technical programs
PREFERRED QUALIFICATIONS
- project management certification (e.g., APM, CSM, PMI-ACP, PRINCE2)
- AWS certification
- Knowledge of the value proposition of the public cloud and cloud engagement delivery (IaaS, PaaS, SaaS)
- Experience working with and presenting to C-level executives, IT, and lines of businesses across organizations 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, GA, Atlanta - 131,200.00 - 177,600.00 USD annually
USA, IL, Chicago - 131,200.00 - 177,600.00 USD annually
USA, NY, New York - 144,400.00 - 195,400.00 USD annually
USA, TX, Austin - 131,200.00 - 177,600.00 USD annually
USA, TX, Dallas - 131,200.00 - 177,600.00 USD annually
USA, VA, Arlington - 131,200.00 - 177,600.00 USD annually
USA, WA, Seattle - 131,200.00 - 177,600.00 USD annually
Salary Context
This $131K-$195K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($163K) sits 23% below the category median. Disclosed range: $131K to $195K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Amazon Web Services AI Hiring
Amazon Web Services has 64 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, AI Software Engineer. Positions span New York, NY, US, San Francisco, CA, US, Cupertino, CA, US. Compensation range: $160K - $299K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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