MLOps Engineer vs AI Engineer:
Infrastructure vs Implementation
Both roles make AI work in production. The difference is focus:
MLOps engineers keep AI running. AI engineers build AI features.
AI Native Engineer Community Access
Infrastructure vs Features:
Two Paths to AI Production
You hear both titles at AI companies and aren't sure which involves more coding versus infrastructure work.
Job descriptions overlap significantly—both mention deployment, monitoring, and production systems.
You're not sure which role fits your DevOps/infrastructure background versus application development experience.
Here's How They Differ
The AI Career Accelerator
MLOps and AI Engineering are complementary disciplines. MLOps focuses on the infrastructure that makes AI reliable. AI Engineering focuses on building the features that users interact with.
MLOps Engineer Focus
Model deployment pipelines, experiment tracking, model monitoring, infrastructure automation, and ML system reliability
AI Engineer Focus
Building AI-powered features, LLM integration, RAG systems, AI agents, and application development
The Overlap
Both need production deployment skills. AI engineers use infrastructure MLOps builds. MLOps enables AI engineers to ship faster.
Meet Your Mentor
When I started in tech, I was based in the Netherlands with no connections and only thousands of video game hours under my belt. Not exactly the ideal starting point.
My first tech job was software tester. One of the most junior roles you can start with. I was just happy someone took a chance on me.
I kept learning. Kept pivoting. But what actually accelerated my career wasn't more certifications or more code. It was learning to solve problems that matter and proving beyond a doubt that what I built solved real problems. That's the skill that stays future-proof, even with AI.
I've since worked remotely for international software companies throughout my career. Proof that the high-paid remote path is possible for anyone with the right skills and motivation. In the end, I went from a $500/month internship to 6 figures as a Senior AI Engineer at GitHub.
Now I teach over 22,000 engineers on YouTube. Becoming an AI-Native Engineer is a system I lived through and offer to you today.
Real Results
Vittor
AI Engineer
Landed his first AI Engineering role in 3 months
"The coaching played a huge part in my success. I focused on AI fundamentals, the certification path, and soft skills like professional writing. Having access to expert guidance gave me confidence during interviews and helped me feel I was on the right path.
I built my own platform (simple but functional) and deployed it on AWS. I used it in my portfolio and showcased it during interviews. The way complex topics were explained, especially the restaurant analogy for AI systems, really stuck with me. Focusing on doing the basics well was absolutely essential."
What You Will Get
Personalized Roadmap & Career Strategy
A custom plan tailored to your background, goals, and timeline. No generic advice.
Weekly 1:1 Coaching Calls
Direct access to Zen for guidance, project feedback, and answers to your questions.
Portfolio-Ready AI Projects
Build production-grade AI applications to showcase to employers. Work that gets you hired.
Interview Prep & Mock Interviews
Practice technical and behavioral interviews. Learn what hiring managers look for.
Resume & LinkedIn Optimization
Transform your online presence to attract recruiters. Stand out from other applicants.
Community Career Support
Join the AI Native Engineer community. Not seeing results yet? You stay and keep going. We're with you through the ups and downs.
Both Roles Are Critical for AI in Production.
Every month you delay can cost you thousands in lost earning potential. While you're watching tutorials, others are landing $120K+ AI Engineering roles.
I can only work with a limited number of 1:1 clients at a time to ensure you get the personalized attention you deserve.
Frequently Asked Questions
What is the main difference between MLOps engineers and AI engineers?
MLOps engineers focus on infrastructure and operations: building CI/CD pipelines for ML, managing model versioning and deployment, setting up experiment tracking, monitoring model performance, and ensuring ML systems are reliable. AI engineers focus on building features: integrating LLMs into applications, building RAG systems, creating AI agents, and shipping AI-powered products. MLOps is about 'how do we reliably run AI?' AI Engineering is about 'what AI features do we build?'
What does daily work look like in each role?
MLOps Engineer: Configuring deployment pipelines, debugging model serving issues, setting up monitoring dashboards, managing model registries, optimizing inference costs, automating retraining workflows. AI Engineer: Writing application code, integrating LLM APIs, building RAG pipelines, debugging prompt issues, shipping features to users, evaluating AI output quality. MLOps spends more time in infrastructure tools. AI Engineering spends more time in application code.
What skills do each role require?
MLOps Engineers need: Docker/Kubernetes, CI/CD tools (GitHub Actions, Jenkins), ML platforms (MLflow, Kubeflow, SageMaker), monitoring systems, infrastructure as code (Terraform), cloud platforms deeply. AI Engineers need: Python application development, LLM APIs (OpenAI, Anthropic), RAG systems, vector databases, prompt engineering, application architecture. Both benefit from understanding the other domain—AI engineers who know MLOps ship faster.
Do MLOps engineers or AI engineers earn more?
Comparable salaries at similar levels. Senior MLOps Engineers: $150K-$230K. Senior AI Engineers: $150K-$250K. AI Engineers have a slight premium due to higher demand and newer field. MLOps salaries are boosted by DevOps/infrastructure scarcity. Both are well-compensated. Choose based on work preference, not salary—the difference isn't significant enough to matter.
What's the typical career path for each role?
MLOps often comes from: DevOps → MLOps, Data Engineering → MLOps, or Platform Engineering → MLOps. Career progression: MLOps Engineer → Senior → Staff → ML Platform Lead. AI Engineering often comes from: Software Engineering → AI Engineering, Full-Stack → AI Engineering. Career progression: AI Engineer → Senior → Staff → Principal AI Engineer. Some engineers move between roles as companies' needs evolve.
How do I know which path is right for me?
Choose MLOps if you enjoy infrastructure work, automation, reliability engineering, and making systems run smoothly. You'll spend time on pipelines, monitoring, and operational challenges. Choose AI Engineering if you enjoy building features, working with AI capabilities, and shipping products users interact with. You'll spend time on application code, AI integrations, and feature development. Infrastructure people → MLOps. Application builders → AI Engineering.
What background leads to each role?
MLOps typically attracts people from DevOps, platform engineering, or data engineering backgrounds. Infrastructure experience is the strongest foundation. AI Engineering typically attracts software engineers, backend developers, or full-stack developers. Application development experience matters most. Both paths are accessible to career changers, but the foundational skills differ significantly.
How long does it take to become job-ready for each role?
MLOps with DevOps background: 3-6 months learning ML-specific tools and patterns. AI Engineering with software engineering background: 3-6 months learning LLM APIs and AI application patterns. Both transitions leverage existing skills—you're adding domain specialization, not starting over. Without relevant background, expect 6-12 months for either path.
What if I don't land interviews in 90 days?
You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.
How is this different from online courses?
Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.
What's the investment for 1:1 coaching?
Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.
Can I do this while working full-time?
Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.
Ready to Land Your AI Role?
Stop watching others succeed. Start building your AI career today.
30-minute strategy call • Limited spots available