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About This Role
DESCRIPTION
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The Amazon Web Services (AWS) Professional Services (ProServe) Strategic Sales team is seeking a Sr. Engagement Manager to join our team. In this role you’ll identify, evaluate, negotiate, and manage complex deals and strategic partners through negotiation and contractual closure, with a focus on VMware migrations. You will be responsible for driving AWS Professional Services contracts (ProServe Orders (PSOs)), understanding and defining business outcomes, building trust and managing the lifecycle of cross\-functional deal cycles end to end. You will remain engaged in sales to delivery handover to ensure that we are delivering the agreed customer business outcomes as outlined in the PSO.
This role delivers outcomes with cross\-organizational impact, engaging customer executives, IT teams, and lines of business to achieve business outcomes, increase the adoption of AWS services, and help enable strategic relationships. Your focus may be on a single critical program or broader initiatives spanning organizations or geographies in support of larger business objectives. The ProServe Strategic Sales \- Sr. Engagement Manager will collaborate with ProServe Account Executives, Delivery Consultants, Partners, and Account Teams across ProServe, Sales, and the Worldwide Services Organization to drive customer success and business growth. This is a fast\-paced environment, requiring adaptability and the ability to manage multiple engagements in parallel.
Your experience with Enterprise customers will be highly valued in this position. You’ll bring excellent problem\-solving and communication skills to the table, along with a strong track record of delivering impactful results. Your expertise in cross\-team collaboration, project management, and executive presentations will be crucial for success in this role. A strong record of customer advocacy and influence without authority is important for this role. A keen sense of ownership, drive, and scrappiness is a must.
Your experience in selling services within the technology/consulting sector will equip you with the ability to translate technical concepts into business value for customers. You will demonstrate proficiency in business development, executing sales methodologies, bid management and managing CRM systems, coupled with strong analytical, problem\-solving, and project management abilities. Performance will be measured against key metrics including but not limited to Revenue, Billable Bookings, and deal cycle duration, and quantifiable business measures.
Key job responsibilities
Executing and defining strategies that complement the practice and product strategy with limited oversight, and developing the mechanism for getting and delivering customer and partner feedback
Closing or significantly growing large or more complex medium\-sized business opportunities, and influencing and executing complex VMware migration deals across multiple teams and organizations
Orchestrating rapid proposal development and fast internal approval processes, and driving adoption of pre\-approved pricing models and specialized deal structures
Coordinating with cross\-functional teams for commercial structuring and executive approvals, and supporting consistent execution of milestone\-based delivery and investment release schedules
Creating proposals, securing customer sign\-off on ProServe Orders (PSO), and ensuring successful sales to delivery handover
Contributing to strategic documents (e.g. OP1, PR/FAQ, GTM) and owning reporting on forecasting and planning business impact, with focus on actuals vs. plan targets
Enabling our sales teams by providing guidance and best practices
Advocating business opportunities to stakeholders up to the Director level and above (VP and SVP), and serving as a subject matter expert in your domain, consulted by levels above and below
Mitigating risks before they appear on the roadmap, and advocating for customers while balancing AWS business objectives
A day in the life
On any given day, you’ll be managing several VMware migration opportunities in parallel at different stages of the deal cycle. You might start by meeting with a customer’s IT and executive stakeholders to understand their VMware estate, migration drivers, and desired business outcomes, then translate that into a ProServe Order (PSO) scope and commercial structure. You’ll partner with ProServe Account Executives, Delivery Consultants, and Partners to shape the proposal, orchestrate rapid internal approvals, and apply pre\-approved pricing models and specialized deal structures to accelerate closure.
As deals progress, you’ll negotiate terms, secure customer sign\-off, and drive a clean sales\-to\-delivery handover so the delivery team can execute against milestone\-based schedules and investment release plans. Throughout, you’ll surface and mitigate risks early, keep forecasting and pipeline reporting accurate against plan, and serve as the subject matter expert for internal stakeholders. Expect frequent collaboration across ProServe, Sales, and the Worldwide Services Organization as you move opportunities from qualification to contractual closure.
About the team
The AWS Professional Services organization is a global team of experts that help customers realize their desired business outcomes when using the AWS Cloud. We work together with customer teams and the AWS Partner Network (APN) to execute enterprise cloud computing initiatives. Our team provides assistance through a collection of offerings which help customers achieve specific outcomes related to enterprise cloud adoption. We also deliver focused guidance through our global specialty practices, which cover a variety of solutions, technologies, and industries.
BASIC QUALIFICATIONS
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- 5\+ years of external or internal customer facing, complex and large scale project management experience
- Experience managing and delivering large\-scale enterprise IT projects
- Experience negotiating complex deals with customers and partners or equivalent
- Experience with IT compliance and risk management requirements (e.g. security, privacy, SOX, HIPAA etc.)
- Experience with software development life cycle (sdlc) and agile/iterative methodologies
PREFERRED QUALIFICATIONS
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- Experience with compliance \& security standards including PCI DSS, ISO 27001, HIPAA, and NIST
- Experience implementing AWS services in a variety of distributed computing environments
- Experience integrating AWS cloud services with on\-premise technologies (e.g., Microsoft, IBM, Oracle, HP, SAP)
- Experience with enterprise systems including SAP, Oracle, and custom applications
\- Knowledge of infrastructure\-as\-a\-service (IaaS) cloud computing transition challenges
- Experience managing customer relationships and advocating to Director\-level and above (VP/SVP) stakeholders
- Experience with VMware technologies and virtualized/hybrid computing environments
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, VA, Arlington \- 153,600\.00 \- 207,800\.00 USD annually
USA, VA, Herndon \- 153,600\.00 \- 207,800\.00 USD annually
USA, WA, Seattle \- 153,600\.00 \- 207,800\.00 USD annually
Salary Context
This $153K-$207K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($180K) sits 16% below the category median. Disclosed range: $153K to $207K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Amazon Web Services AI Hiring
Amazon Web Services has 93 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span New York, NY, US, Arlington, VA, US, Cupertino, CA, US. Compensation range: $160K - $350K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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