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About This Role
DESCRIPTION
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Are you a builder? Do you thrive working with GenAI solutions? Do you love owning a product end\-to\-end, scaling distributed systems, shipping software fast, growing high\-performing engineering teams, and driving technical strategy that directly impacts customers? If combining these skills — building software solutions around AI workflows that solve real\-world problems at speed — excites you, this role is for you.
Amazon's Ops Tech Solutions (OTS), Data Anchor organization is seeking an experienced Software \& Systems Development Manager to lead our software and agentic development team(Decision Intelligence) within the OTS Data ANCHOR organization. This role will own the strategy, development, and delivery of AI\-powered agent solutions that automate and optimize operational workflows across Amazon's global IT support and field operations ecosystem. You will turn POC ideas into production grade and solutions.
The Decision Intelligence team builds and deploys agentic AI solutions that integrate with enterprise platforms (ServiceNow, internal orchestration systems) to drive measurable operational efficiency gains at scale. Our agents currently deliver thousands of hours in annual labor savings, and operate across 74\+ global sites. As our portfolio expands, we need a hands\-on technical leader who can scale both the technology and the team who is a global team from France, Luxembourg to the US.
You will lead a team of engineers (Software Development Engineers, Systems Development Engineers, and Solutions Architects) responsible for the full lifecycle of AI agent development — from ideation and prototyping through production deployment and continuous improvement. You will partner closely with Product, Data Science, Data Engineering, and cross\-functional engineering teams to define the roadmap, drive technical excellence, and deliver measurable business impact.
Key job responsibilities
- Lead and grow a team of SDEs, Systems Development Engineers, and Solutions Architects; provide mentorship, career development, and performance management
- Own the technical roadmap for the Agentic AI portfolio including solutions like, MCM Creation Agent, Severity Triaging Agent, ASML Agent, Printer Agent Recommendation Engine, and CSD AutoCut automation and more.
- Drive architecture and design of intelligent agent systems leveraging AWS services (Bedrock, Lambda, Step Functions), LLM orchestration, and enterprise integrations (ServiceNow, Agent Orchestrator)
- Deliver measurable outcomes — reduce operational labor, improve accuracy, and increase automation coverage across OTS's global IT support operations
- Partner cross\-functionally with the DSE Tech Team, Product Management, Field IT leadership, and Service Desk stakeholders to align agent capabilities with customer needs
- Manage the full development lifecycle from proof\-of\-concept through production deployment, including pilot design, success metrics, safety reviews, and scaled rollout
- Raise the technical bar by establishing engineering best practices, code review standards, CI/CD pipelines, and operational excellence mechanisms for AI/ML systems
- Communicate effectively with senior leadership on strategy, progress, risks, and business impact through mechanisms like MBRs, sprint reviews, and roadmap presentations
About the team
The OTS Data ANCHOR team is the data, analytics, and intelligent automation engine for Amazon's Ops Tech Solutions organization — supporting global IT field operations, service desk, and infrastructure teams. ANCHOR operates across three pillars: Reporting \& Analytics, Data Enablement, and Data Modeling \& Intelligence.
The Decision Intelligence team sits within the Data Modeling \& Intelligence pillar and has experienced rapid growth since March 2026\. In just three months, the team has scaled from early\-stage pilots to production\-deployed agents processing thousands of tickets globally, integration with enterprise orchestration platforms, and a portfolio of 5\+ active AI agent initiatives. Our solutions directly impact the productivity of thousands of Amazon technicians and engineers worldwide.
We are a builder culture that moves fast, experiments boldly, and measures everything. If you're passionate about applied AI, operational excellence, and growing high\-performing engineering teams — this is an opportunity to shape the future of intelligent automation at Amazon scale.BASIC QUALIFICATIONS
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- 5\+ years of managing system or software development teams experience
- 7\+ years of relevant hands\-on systems engineering and administrative work in networking, storage systems, operating systems experience
- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field
- Experience leading development life cycle processes and best practices, especially in the areas of deployment automation and monitoring
- Experience with AWS platforms, services, and design patterns
- Experience developing, deploying and managing AI products at scale
PREFERRED QUALIFICATIONS
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- Experience with Agile engineering practices (Kanban, continuous delivery, etc.)
- Experience in creating process improvements with automation and analysis
- Master's degree or above in engineering, management, or technology, or Master's degree in business administration, finance, economics, computer science, data science, engineering, or other related field
- Experience managing geographically distributed teams or working with global operations stakeholders
- Familiarity with AWS AI/ML services (Bedrock, SageMaker, Lambda, 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, TX, Austin \- 166,300\.00 \- 225,000\.00 USD annually
Salary Context
This $166K-$225K range is above 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.com, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($195K) sits 8% above the category median. Disclosed range: $166K to $225K.
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.com AI Hiring
Amazon.com has 102 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Product Manager, Data Scientist. Positions span New York, NY, US, Palo Alto, CA, US, Bellevue, WA, US. Compensation range: $129K - $300K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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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