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About This Role
DESCRIPTION
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This position is part of the AWS Specialist and Partner Organization (ASP). 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. Partner teams own the strategy, recruiting, development, and growth of our key technology and consulting partners. Together they provide our customers with the expertise and scale needed to build innovative solutions for their most complex challenges.
Within ASP, the AI \& Strategic Partner Engineering team is seeking a Senior Systems Development Engineer to create solutions that drive the adoption of SAP workloads on AWS and ensure those workloads run with world\-class resilience. You'll design, develop, and own back\-end subsystems that solve complex distributed system challenges for SAP customers. You'll build and manage AI agents that handle operational tasks (debugging production issues, maintaining infrastructure, and automating repetitive workflows) so engineers can focus on high\-judgment work. You'll also work on customer\-facing APIs used by millions of applications worldwide.
The ideal candidate has deep technical skills in SAP applications and cloud computing, is conversant in both SAP's agentic technologies (Joule, BTP AI Core) and AWS AI services (Bedrock, Amazon Q), and can apply these tools practically to solve real operational problems. Your SAP domain expertise (SAP BASIS, ABAP, and systems architecture knowledge) is essential to making a significant impact. This role demands a self\-starter mindset, rapid experimentation, and expertise in resilience engineering, ideal for engineers who love learning, challenging norms, and building systems that enable other builders to ship faster and safer.
Key job responsibilities
You'll own your team's systems end\-to\-end: proactively identifying risks, limitations, and deficiencies; decomposing complex problems into straightforward projects others can deliver in parallel; and setting the standards for engineering and operational excellence that your team adopts. You'll define the technical direction for your team's systems, own architectural decisions, and drive cross\-team alignment, building resilient, partner\-driven solutions alongside senior engineers and architects.
You'll design, implement, deploy, and maintain innovative distributed system software that enhances service security, durability, availability, performance, and cost\-efficiency for SAP workloads on AWS. Your SAP domain expertise, including SAP BASIS, ABAP, and knowledge of SAP systems architecture, is essential to designing integrations that meet the needs of enterprise customers and the SAP community.
You'll create and manage purpose\-built AI agents that perform operational tasks: triaging alarms, diagnosing root causes, analyzing build pipelines, managing fleet health, and tracking fixes. You'll use AI tools daily to debug complex distributed failures, build and maintain infrastructure, and generate documentation, treating AI as a force multiplier for systems engineering work. You'll be conversant in SAP's agentic ecosystem (Joule, BTP AI Core, SAP AI Launchpad) and AWS AI services (Bedrock, Amazon Q, Step Functions) to design solutions that bridge both platforms.
You'll design, build, and harden AI\-assisted systems and workflows, ensuring they run reliably in production, degrade gracefully under failure, and meet the resilience bar required for mission\-critical SAP workloads. You'll design operational patterns that leverage AI for monitoring, incident response, and automation to improve system resilience and reduce complexity for other builders.
You'll own full\-stack delivery from design through production, emphasizing high availability, performance, fault tolerance, and customer delight. You'll build detailed technical specifications, lead architecture and operational reviews, and partner with cross\-functional teams (Product, Security, AI Research) to influence roadmaps through data and credibility. You represent your team externally to partner service teams and leadership. All team members actively participate in product definition, architecture reviews, iterative development, code review, and operations. Clear, professional communication with teammates, customers, and partners is essential.
A day in the life
You'll identify partner pain points and make architectural decisions that blend resilience, scalability, security, and innovation. You'll build agents that investigate production issues end\-to\-end, validate them through testing, and iterate based on real operational data. You'll lead design reviews, troubleshoot complex distributed failures across system boundaries using AI\-assisted diagnostics, and document decisions to empower future teams. When you recognize that the most appropriate solution requires expertise you lack, you own the problem but partner with experts to ensure the correct solution is built. Occasional travel may be required based on engineering work requirements with partners.
About the team
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge\-sharing and mentorship. Our senior members enjoy one\-on\-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future.
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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- 6\+ years of non\-internship professional software development experience
- 6\+ years of designing or architecting (design patterns, reliability and scaling) of new and existing systems experience
- Bachelor's degree
- 5\+ years of programming with at least one modern language such as C\+\+, C\#, Java, Python, Golang, PowerShell, Ruby experience
- • Expertise in SAP Domain including SAP ABAP programming language skills, as well as knowledge of SAP systems (like SAP Business Suite, S/4HANA, SAP Business Warehouse, SAP HANA, SAP Business Objects, etc.) and their architecture and infrastructure needs
- • Experience building with AI/ML technologies including large language models, generative AI, or agentic systems — with demonstrated ability to apply AI tools to operational tasks such as debugging, monitoring, or infrastructure management
- Experience leading the delivery of technology solutions across multiple teams, including decomposing complex problems into projects deliverable by others
PREFERRED QUALIFICATIONS
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- Deep knowledge of AWS services including compute, storage, networking, security, databases, machine learning, and serverless technologies. Experience architecting solutions spanning multiple AWS service domains in a multi\-tiered, distributed environment (Service Oriented Architecture)
- Experience with both SAP's agentic technologies (Joule, BTP AI Core, SAP AI Launchpad) and AWS AI services (Bedrock, Amazon Q, Step Functions)
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, Seattle \- 151,200\.00 \- 204,600\.00 USD annually
Salary Context
This $151K-$204K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $151K to $204K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. 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: $247,800; 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, Research Scientist, AI Product Manager. Positions span Seattle, WA, US, San Francisco, CA, US, Arlington, VA, US. Compensation range: $177K - $295K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $253,500, 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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