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About This Role
We put AI first
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We're looking for a Principal Generative AI Engineer to build AI systems that connect development workflows with production observability. You'll design and ship agentic tooling that helps engineers understand code, assess the impact of changes before they hit production, and act on real runtime signals.
Your role at Dynatrace
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We are looking for a visionary, technically excellent engineer who is ready to design and ship production\-grade agentic AI systems that bridge the gap between code context and runtime signals. You should thrive on a foundation of freedom, feedback, and responsibility, bringing a startup\-like drive backed by the reach of a global platform.
- Design, build, and ship agentic AI systems and tooling that helps engineers understand code, assess the impact of changes before they hit production, and act on real runtime signals.
- Build production LLM systems end to end, managing prompting, tool calling, retrieval, memory management, and the agent loops that tie them together.
- Define the evaluation strategies, metrics, and datasets that make agent quality measurable, ensuring we ship on evidence and catch regressions across model and prompt changes.
- Connect development workflows with production observability, turning code context and runtime signals into reliable, actionable insight, and collaborate with product, design, and platform teams to identify developer problems.
- Set technical strategy and architectural direction for the team's AI systems, mentor engineers across the organization, and take full ownership of systems using Dynatrace to monitor and optimize them.
What will help you succeed
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You are a seasoned technical leader with a strong software engineering foundation and proven hands\-on experience deploying complex LLM applications. You possess the engineering judgment to navigate ambiguous, fast\-moving spaces and set clear architectural direction for your team.
- Degree in Software Engineering or equivalent practical experience in software development, combined with a track record of technical leadership and the judgment to set direction in an ambiguous, fast\-moving space.
- Extensive experience shipping production systems that use LLMs, including prompting, tool calling, evaluation, and iteration.
- A strong foundation in at least one of: developer tooling (IDEs, compilers, static analysis, code intelligence), AI/ML engineering, or large\-scale distributed systems.
- Hands\-on experience with agentic patterns, specifically planning, tool use, retrieval, and memory management.
- The ability to evaluate and critique AI\-generated output, understanding why a model is wrong, not just that it is, with familiarity with observability and the Dynatrace platform as a strong advantage.
Why you will love being a Dynatracer
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- Dynatrace is a leader in unified observability and security.
- We provide a culture of excellence with competitive compensation packages designed to recognize and reward performance.
- Our employees work with the largest cloud providers, including AWS, Microsoft, and Google Cloud, and other leading partners worldwide to create strategic alliances.
- You'll get to work at the forefront of innovation with Dynatrace Intelligence—the industry's first agentic operations system. Bringing together deterministic and agentic AI, it helps teams understand what's happening, why it matters, and what to do next— automatically.
- Over 50% of the Fortune 100 companies are current customers of Dynatrace.
Compensation and Rewards
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The base salary range for this role is $146K \- $220K . When determining your salary, we consider your experience, skills, education, and work location. Our total compensation package includes unlimited personal time off, an employee stock purchase plan, and a reward system.
We also offer medical/dental benefits and a company\-matched 401(k) plan for retirement.
Equal Employment Opportunity
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Dynatrace provides equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, veteran status, or any other protected characteristic. We actively foster an inclusive workplace that celebrates differences and promotes accessibility, collaboration, and growth for all.
Note to Recruiters and Agencies : Thank you for your interest in Dynatrace. Please note that we do not accept unsolicited agency resumes —do not forward them via our website or directly to Dynatrace employees. Dynatrace will not pay fees for unsolicited resumes, and any resumes received this way will be considered the property of Dynatrace.
Benefits and work\-life perks
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We offer best\-in\-class core rewards, including paid time off, financial security benefits, retirement savings plans, and health insurance. Beyond that, you’ll get other benefits and work\-life perks designed to make your ride with us even more rewarding.
#### Mental health support
Our Employee Assistance Program, powered by Telus Health, offers support for you and your family members.
#### Wellness Days
Four company\-designated extra paid days off for you to recharge batteries.
#### Flexibility
Our hybrid working model and flexible working hours offer you the flexibility you need.
#### Employee Stock Purchase Plan
Purchase company stock ( NYSE:DT ) at a discounted price and become a shareholder.
#### Learn \& develop
Company\-wide learning perks, designated team's learning days, and more.
#### Volunteering day
A day of paid volunteer time to support a community or cause you care about.
#### Regular team events
We host Global Culture Parties, Family \& Friends at Work Day, Global Breakfasts, Green Weeks, Pride Month, and beyond!
#### International vibe
Most of our offices and teams are proudly multicultural. English is our shared language, but we embrace and learn from each other's cultures.
Rewards vary depending on your employment type. Some benefits and perks also differ by location — explore your city to see what’s available there.
About Dynatrace
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Dynatrace (NYSE: DT) is the leading AI\-powered observability and security platform. We're advancing observability for today's digital businesses, helping transform modern digital ecosystems' complexity into powerful business assets.
Our AI\-driven insights cut through the noise, allowing customers to focus on what truly matters by automating manual tasks and resolving issues with pinpoint accuracy. Dynatrace offers simplicity, clarity, and reliability at scale to ensure teams can make informed decisions, minimize downtime, and drive their business forward with confidence.
Salary Context
This $146K-$220K 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 Dynatrace, 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: $146K to $220K.
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.
Dynatrace AI Hiring
Dynatrace has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $220K - $220K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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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