Programs for engineers · 2+ years in production

Advanced AI programs for tech professionals

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AI writes more of the code every month, and your job is turning into checking it. TripleTen’s advanced programs take you from feature work to designing, building, and running AI systems in production, with 1:1 mentorship from senior engineers.

TripleTen runs two advanced programs for engineers with 2+ years of production experience: AI & Machine Learning (4.5 months) and AI Systems Engineering (5 months), at about 20 hours a week. Start with a short skills audit to check your level and get a program recommendation.

Start with a skills audit

Not for beginners. If you’re starting from zero, TripleTen runs entry-level programs designed for exactly that starting point.

Sound familiar?

What engineers told us in interviews before they start

And how the programs answer it.

  • “I spend more time checking what AI wrote than writing code.”

    Learn to design the system around the model: context, evals, and guardrails.

  • “Management thinks the LLM is a magic wand.”

    Learn to estimate what AI can do, what it costs, and explain it to the business.

  • “I want to build AI, not just use it.”

    Build ML, LLM, and agent systems end to end, then deploy them.

  • “I’m stuck closing tickets and never design anything.”

    Graduate with one working system you designed, built, and deployed, then presented in live project reviews.

  • “Every AI job wants AI experience I can’t get at work.”

    Five projects you present in live reviews, plus mock interviews with working engineers, give you that experience to show.

  • “Self-study keeps stalling.”

    Structured projects, a weekly 1:1 with your instructor, and a live review of every project.

How it works

How TripleTen advanced programs work

Build alongside experienced engineers

Your fellow students are all engineers with 2+ years in production.

Five projects, one production system

Each project extends the last, so you finish with one working system you designed, built, deployed, and presented in live project reviews.

1:1 with your instructor

A 30-minute 1:1 with your instructor every week. It’s optional, and you’re in charge of the agenda.

Built around a full-time job

Designed for engineers working 40 hours a week or more. There are no fixed class times, and all live sessions run in the evenings and on weekends.

Career support

A career coach offers resume and LinkedIn support, unlimited AI mock interview practice, up to 10 live mock interviews, and support for up to three months after graduation.

Employer reimbursement ready

Reimbursement documentation for your employer on request, and a full refund if you withdraw within your first 14 days.

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See where you’d start

Book a call with an admissions advisor to talk through your experience and goals, and find the program that fits you.

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Which AI engineering program fits you?

Choose AI & Machine Learning to build models, LLM applications, and agents. Choose AI Systems Engineering to design and scale the distributed systems and AI pipelines around them.

ProgramWhere you are nowThe gap you closeRoles you can targetLengthTuition
AI & Machine LearningYou ship production code or you work with data. Most students here come from software engineering, data engineering, DevOps, QA engineering, or quantitative work.Classical machine learning, knowing what to build with AI and what to build without it, LLM applications and agents, and MLOps.Machine Learning Engineer, AI Engineer, MLOps Engineer4.5 months at about 20 hours a week$12,600 upfront$17,950 financed$20,000 in installments
AI Systems EngineeringYou run production systems already. Most students here come from backend and full-stack engineering, and from DevOps, SRE, cloud, and platform work.Cloud and DevOps, breaking a system into services, observability, security and compliance, and scaling.AI Platform Engineer, AI Infrastructure Engineer, AI Architect5 months at about 20 hours a week$12,600 upfront$17,950 financed$20,000 in installments
Enrolling now

Two advanced programs for engineers in production

For engineers who want to build ML and agent systems themselves.

AI & Machine Learning

You add AI and machine learning skills on top of the engineering experience you already have: building, evaluating, and deploying models. The first project hands you structured inputs and hints. By the fifth, you receive a business request and nothing else, and it’s your task to choose the tools, the architecture, and the approach.

  • RAG architecture
  • Agentic orchestration (LangChain, CrewAI, ADK)
  • Fine-tuning (SFT, LoRA)
  • LLM evaluation and guardrails

Roles you can target: Machine Learning Engineer, AI Engineer, MLOps Engineer. Typical range $132,000–$215,000, top of the range around $274,000.

$12,600 upfront

  • 4.5 months at about 20 hours a week
  • 2+ years in production

For engineers stuck in feature work who want to design and run systems.

AI Systems Engineering

You add production AI on top of the role you already hold, or move from backend, infrastructure, or data into AI systems. Each project builds on the last and ends in one production AI system.

  • Agent orchestration
  • Event-driven architecture
  • Relational data modeling
  • System decomposition
  • Cloud/DevOps
  • API and service architecture

Roles you can target: AI Platform Engineer, AI Infrastructure Engineer, AI Architect. Typical range $180,000–$254,000, top of the range around $297,000.

$12,600 upfront

  • 5 months at about 20 hours a week
  • 2+ years in production

Source: Glassdoor, total yearly pay in the United States for senior-tier roles, as of September 2026. These are the salary ranges employers currently pay for these roles. They are not a projection of what you personally will earn.

Work with a senior engineer from day one

Day 1

Meet the instructor you’ll work with one-on-one every week.

During the program

Your instructor reviews your architecture: how you solved each problem and where the system could fail.

Every project

Present your work to a practicing engineer and walk them through your decisions, the same way you would in a system design interview.

Mohamed CherifInstructor

Principal / Staff Software Engineer

Mohamed Cherif

20+ years building GPU-accelerated media pipelines, SDKs, and computer vision systems at AMD, Microsoft, Amazon, and Skype.

Dr. Gönen ErenCurriculum Author

Founder & CTO; most recently .NET APIs on AWS

Dr. Gönen Eren

20+ years building distributed IoT backends on Azure, high-throughput telemetry pipelines, .NET APIs, and workflow engines on AWS. Ph.D. in Computer Science, master’s from ETH Zürich.

Ahmed AbouzeidInstructor

Sr. Manager, Software Development

Ahmed Abouzeid

15+ years shipping distributed systems, platform infrastructure, and generative AI products at scale across AWS and Amazon.

Amish SuchakCurriculum Author

Data Science Lead, XSOLIS Co-Founder, Percruit

Amish Suchak

Building machine learning models and deploying them to production at XSOLIS (AI for healthcare). Teaching at MIT Professional Education since 2021.

Tejashree KhotInstructor

Software Development Manager

Tejashree Khot

Building backend and distributed systems for Alexa (Amazon). Led LLM-powered API orchestration, connecting large language models to 200+ APIs.

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Your options

How do TripleTen advanced programs compare to certificates and courses?

OptionGood forWhat’s usually missing
Cloud AI certificates (AWS, Google, Microsoft)Proving you know one vendor’s servicesDesigning a full system, and someone reviewing it
Course marketplaces (Coursera, DataCamp, Udemy)Low-cost theory on a specific toolProduction-scale projects and feedback
Self-study with an AI assistantSpeed and zero costA reviewer, structure, and proof an employer can see
Beginner-level AI coursesPeople new to codingMaterial at a senior engineer’s level
TripleTen advanced programsEngineers moving into senior AI roles with mentorship and deployed projectsA beginner entry point or a low price: tuition starts at $12,600, and you need 2+ years in production

New to tech, or less than 2 years in production? Start with our programs for beginners.

Programs for beginners

Frequently asked questions

What is the best AI engineering course for experienced software engineers?

Look for a program that assumes production experience, reviews your work, and ends with systems you deployed. TripleTen’s AI & Machine Learning (4.5 months) and AI Systems Engineering (5 months) are built for engineers with 2+ years of recent, hands-on production experience, and every project ends in a live review with a practicing engineer.

How do I move from backend engineering into AI engineering without quitting my job?

Add production AI skills on top of the backend work you already do. AI Systems Engineering is built for backend and full-stack engineers: over 5 months at about 20 hours a week, you design, build, and deploy one production AI system across five projects, and all live sessions run in the evenings and on weekends. If you’d rather build models, LLM applications, and agents, AI & Machine Learning covers that in 4.5 months.

I’m a DevOps or SRE engineer. Which program fits me?

Most DevOps, SRE, cloud, and platform engineers choose AI Systems Engineering. You keep the infrastructure you already know and add what runs on top of it: agent orchestration, event-driven architecture, service design, and AI pipelines in production. If you already know a topic like cloud or CI/CD, you show it at the start of a project and skip straight to building.

I’m a data analyst, and my models never leave the notebook. Which program helps?

AI & Machine Learning. It takes you from a model in a notebook to a deployed system: classical machine learning, LLM applications and agents, evaluation, and MLOps. You’ll need solid programming in a modern language and recent hands-on work with production code or data.

I was handed an AI project at work, and the prototype breaks on real data. Can this help?

Yes. Both programs focus on the system around the model: context, evaluation, guardrails, observability, and scaling. Your instructor reviews your code and architecture in a 30-minute 1:1 every week, and each project ends in a live review, so someone checks your work. Choose AI & Machine Learning if your gap is models and agents, and AI Systems Engineering if it’s architecture, operations, and scale.

AI at my company is built by another team. How do I get AI experience I can show?

You build it in the program. Across five projects, you design, build, and deploy one working AI system in a real repository and present each project to a practicing engineer. You finish with a system you can walk an interviewer or your manager through, plus written feedback on every decision.

I keep failing system design interviews. How does the program help?

Every project ends in a 60-minute live review where you present your architecture and explain your trade-offs to a practicing engineer, which is the skill a system design round tests. Career support adds up to 10 live mock interviews (screening, technical, recruiter, and behavioral) with instructors and your coach, and debriefs after your real interviews. TripleTen doesn’t promise a job, but you’ll practice every round before it counts.

Do I write the code myself or with an AI assistant?

You write the code yourself in a real repository: you start from an existing codebase and submit pull requests. Before each live review, your work goes through automated tests and a reviewer, and then you walk your instructor through it. You need to understand every line you ship.

I already know a lot of this. Will I sit through material I know?

No. At the start, you and your instructor list the skills you’re missing for your target role, and that list shapes your projects. If you already know a topic, show it at the start of a project and skip to building. Project hours are fixed; theory is there when you need it.

Is an AI engineer bootcamp worth it if I already have experience?

Most bootcamps start from zero. TripleTen’s advanced programs don’t: neither one teaches you to code, and neither one is an introduction to the field. Both assume you’ve been building and running real systems for years.

AI & Machine Learning or AI Systems Engineering: which should I choose?

Choose AI & Machine Learning to build models, LLM applications, and agents. Choose AI Systems Engineering to design and scale the distributed systems and AI pipelines those models run in. If you’re not sure which program fits you best, book a call and go through your current stack and background with an admissions advisor.

Can I do it with a full-time job?

Yes. The programs are designed for engineers working 40 hours a week or more. There are no fixed class times, and all live sessions run in the evenings and on weekends. If you can commit to about 20 hours a week, you’ll finish in 4.5 months or 5 months, depending on the program; at fewer hours a week, plan for longer.

How much does it cost, and can my employer pay?

Tuition is $12,600 upfront, $17,950 financed, or $20,000 in installments. TripleTen installments have no credit check and no interest; tuition financing requires a soft credit check. If your employer contributes to tuition, we provide reimbursement documentation on request. You can withdraw within your first 14 days and receive a full refund.

Will AI replace software engineers?

AI is taking over more code writing, and engineers we interviewed say their time is shifting to verifying and testing it. Designing, evaluating, and operating AI systems is the work that grows, and it’s what these programs teach.

I was laid off. Can I join, and how do I pay?

Yes. Admission depends on your production experience, not on whether you’re employed right now. With more hours a week, you can finish sooner, and career support can start from day one on request. TripleTen installments start with a $700 deposit, with no credit check and no interest, and you can withdraw within your first 14 days for a full refund.

My company is starting an AI team. Can this help me move onto it?

It gives you the skills and the work to show for it: one production AI system you built and presented in live reviews. Most students study alongside their current role, and TripleTen provides reimbursement documentation if your employer will cover tuition. Who joins the team is your company’s call.

My Python isn’t strong. Can I still apply?

Yes, if you program well in another modern language. Solid programming is the prerequisite, and an optional Python theory block helps you catch up.

The programs are new. How do I know they work?

These programs are new, and their first cohorts are studying now. They run on the same platform, project review process, and career support as TripleTen’s established programs, which have 2,600+ US graduates. Before you enroll, you can check the instructors’ public profiles, the syllabus, and sample projects on GitHub.

Who teaches: a person or AI?

People. Working engineers run your weekly 1:1s, your live project reviews, and your technical mock interviews. AI systems are what you learn to build.

What happens if a project review doesn’t go well?

You get written feedback and rebook the review, usually within about a week. Retakes are unlimited, and nobody fails out for needing a second attempt.

What does the skills audit check?

The free 3-minute audit asks technical questions about architecture and AI systems. It shows whether the programs fit you, your current level, and your gaps, and it recommends a program. You can then book a call with an admissions advisor to turn the results into a study plan, with no commitment.

What do I get when I finish?

A completion certificate, one production AI system reviewed by practicing engineers, and lifelong access to the curriculum. TripleTen is licensed by the Wyoming Department of Education and registered in California as an out-of-state institution. It doesn’t grant degrees.

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See where you’d start

Book a call with an admissions advisor to talk through your experience and goals, and find the program that fits you.

By clicking ‘Continue’, you agree to TripleTen’s Privacy Policy and Terms of Use.