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About This Role
DESCRIPTION
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Are you a builder that lives and breathes PostgreSQL? Have you spent years tuning vacuum behavior, diagnosing lock contention under heavy OLTP workloads, reasoning about query planner decisions, managing replication topologies, and guiding large\-scale migrations from commercial engines like Oracle and SQL Server? Are you the person your organization turns to when a Postgres system is misbehaving at 3 AM and when leadership needs a whiteboard session on where the data platform is headed next? As a Specialist Solutions Architect for Aurora PostgreSQL, you will be the technical authority that AWS's largest and most complex customers rely on to architect, migrate, and operate PostgreSQL workloads on Amazon Aurora and Amazon RDS.
This is not a generalist database role. You will focus on Aurora PostgreSQL, going deep where others go broad. You will partner directly with customer engineers and CTOs to design well architected production architectures that hold up under real\-world pressure and the operational patterns that distinguish battle\-tested Postgres practitioners from those with only surface\-level familiarity. You will have experience with migrations (Oracle/SQL Server/on premises Postgres to Aurora). You will collaborate closely with the Aurora service team to surface customer patterns, escalate feature gaps, and drive the product roadmap on behalf of the customers you serve.
Equally important, you will operate as a thought leader and force multiplier across the broader Specialist SA organization, mentoring teammates, publishing technical content, presenting at AWS re:Invent and AWS Summits, and establishing repeatable frameworks that raise the bar for PostgreSQL engagements worldwide.
Key job responsibilities
- Serve as the technical advisor for AWS's key customers running or migrating to Aurora PostgreSQL and RDS for PostgreSQL, providing hands\-on architectural guidance from design through production operation.
- Guide customers on Aurora PostgreSQL operational best practices including performance \& scaling, high availability \& durability, monitoring \& observability, maintenance \& patching, backup \& recovery, security and operational hygiene
- Design and validate Aurora PostgreSQL architectures that leverage Aurora\-specific capabilities including storage auto\-scaling, read replica scaling, Global Database, HA/DR, Serverless, ensuring customers realize the full value of the platform.
- Partner with the Aurora service team as the voice of the customer, surfacing recurring pain points, advocating for feature enhancements, and participating in roadmap reviews.
- Engage with customer engineers in whiteboard sessions, architecture reviews, and technical deep dives, providing candid guidance and pushing back diplomatically when a proposed design will not hold up at scale.
- Develop and publish technical thought leadership content including blogs, reference architectures, white papers, and re\-usable artifacts, that elevate the broader PostgreSQL community on AWS.
- Advise customers complex database migrations (Oracle, SQL Server, and other engines to Aurora PostgreSQL).
- Present at key industry events including AWS re:Invent, AWS Summits, PostgreSQL community conferences (PGConf, PGDay), and customer\-facing workshops.
- Stay current with PostgreSQL community developments (new major versions, extensions ecosystem, community tooling) and translate those advancements into actionable guidance for AWS customers.
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 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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- 8\+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data \& analytics) experience
- 3\+ years of design, implementation, or consulting in applications and infrastructures experience
- 10\+ years of IT development or implementation/consulting in the software or Internet industries experience
- Hands on experience with Aurora PostgreSQL, RDS for PostgreSQL, or significant hands\-on time with AWS database and migration services (DMS, SCT).
- Led migrations to Aurora as the technical owner.
- Experience with Aurora\-specific features: storage architecture, fast cloning, Global Database, Performance/Database Insights, and Blue/Green Deployments.
- Experience architecting or operating PostgreSQL solutions at scale (multi\-TB databases, tens of thousands of connections, cross\-region replication).
- Have software development experience (Python, Java, etc.), and fluency with AI tooling (Kiro, Claude Code, Codex, etc.)
PREFERRED QUALIFICATIONS
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- 7\+ years of infrastructure architecture, database architecture and networking experience
- 5\+ years of hands\-on experience designing, operating, or engineering PostgreSQL\-based systems in production environments at scale.
- Expertise in PostgreSQL internals and operational practices: MVCC and vacuum behavior, query planner and optimizer internals, pgvector, indexing strategies (B\-tree, GIN, GiST, BRIN), replication (streaming, logical), extension ecosystem/architecture, and connection management.
- Demonstrated experience with database migrations, including schema conversion, data migration tooling, and performance validation.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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, CA, Mountain View \- 176,600\.00 \- 239,000\.00 USD annually
USA, CA, San Francisco \- 176,600\.00 \- 239,000\.00 USD annually
USA, NJ, Jersey City \- 169,000\.00 \- 228,600\.00 USD annually
USA, NY, New York \- 169,000\.00 \- 228,600\.00 USD annually
USA, TX, Dallas \- 153,600\.00 \- 207,800\.00 USD annually
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-$239K range is above the median for AI/ML Engineer roles in our dataset (median: $184K across 1486 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 2,799 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $175,000 based on 11,128 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $159,385. This role's midpoint ($196K) sits 12% above the category median. Disclosed range: $153K to $239K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
Amazon Web Services AI Hiring
Amazon Web Services has 55 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, Research Engineer. Positions span New York, NY, US, Jersey City, NJ, US, Austin, TX, US. Compensation range: $142K - $284K.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 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 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 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 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). 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 (98) are outnumbered by mid-level (1,283) and senior (1,092) 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 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 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 $200,000. Top-quartile roles start at $252,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 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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