Learn the job by doing the job
Build 4 open-source systems and defend your architecture in a review. Deliver a project with a partner company and graduate with a reference.
Turning coders into engineers top tech companies compete for
Master AI systems engineering and start earning at the top of the market.
Start your 3-minute skills auditAcademic partners
Want a top-tier role? Here's how you get there:
TripleTen brings the training expertise. Nebius Group brings the AI infrastructure leadership—as a core partner of Meta, NVIDIA, and Microsoft.
Get mentored by first-class engineers
Get 1:1 mentorship, coaching, and guidance from Tier-1 architects and Staff engineers at top tech companies.
Build leadership skills
Mentor students in TripleTen’s programs for tech beginners, and build senior-level leadership skills.
Ace the interview
Practice mock technical and HR interviews, optimize your resume, and access TripleTen’s network of hiring partners.
Is this career accelerator for you?
Feature developersStop shipping CRUD features. Start designing distributed systems.
Data, QA & STEM prosTurn your analytical skills towards production-grade AI and cloud engineering.
Self-taught buildersMaster enterprise-scale tools to pass Big Tech interviews.
Roles you’ll grow into
Full-Stack Engineer, Intelligence Systems
$230,000–$325,000Target market comp
Top employers
Market trend
#1fastest-growing job category
Key skills
At-scale threat detectionNovel AI applicationsComplex data processingCross-functional technical leadership
Forward Deployed Software Engineer (AI)
$205,000–$280,000Target market comp
Top employers
Scale
ScaleMarket trend
42xdemand growth since 2023
Key skills
Frontier model deploymentFull-stack architecture (Python/JS)Evals & eval-driven feedbackSystem design
Staff GenAI Backend Engineer
$204,000–$255,000Target market comp
Top employers
Market trend
40–60%salary premium for GenAI / LLM skills
Key skills
RAG patternsMemory routingsScalable service-oriented architecturesAgent planning
Senior Software Engineer, Inference
$300,000–$485,000Target market comp
Top employers
Market trend
$800K+packages in top AI labs (equity)
Key skills
High-performance distributed systemsKubernetesCloud infrastructureLLM inference optimizationRequest routing & batching
Learn directly from practicing Tier‑1 engineers.
Your instructors run production systems at Big Tech and Fortune 500, and know exactly what hiring managers look for.
You work with a senior engineer from day one
Day 1A named engineer, not a queue. One of them holds your context from the first project brief to the last.
While you buildThey review the architecture while it can still change, and name the part of your design that breaks first.
ReviewA 60-minute review on your final system: 15 minutes to calibrate, then 30 on the decisions themselves.
Mohamed CherifPrincipal / Staff Software Engineer
Reviews your workPrincipal-level engineer. Twenty years building GPU-accelerated media pipelines, SDKs, and computer-vision systems at AMD, Microsoft, Amazon, and Skype.
Dr. Gönen ErenExpert .NET/C# Backend Developer
Wrote the programTwenty years engineering fault-tolerant distributed systems and high-throughput data pipelines on AWS and Azure, with a computer-vision PhD behind it. The five projects and what counts as clearing them are his work.
Systems at scale
Distributed systems, cloud infrastructure, and GPU workloads — what changes when traffic multiplies and the easy answers stop working.
When it breaks
Incidents, resilience, and the failure modes you only meet in production. They’ve been paged for them.
The written decision
Architecture records, scaling reviews, risk registers. The documents a staff engineer gets asked for, reviewed by the people who write them at work.
Fortune 500 backgrounds. Public LinkedIn profiles for both, with a signed release before either appeared on this page. Look them up before your call.
Included in the accelerator:
One-on-one mentorshipWeekly sessions to work through project feedback and hone your skills.
Career guidanceCreate a personalized job search strategy and optimize resume, LinkedIn and portfolio.
Alumni networkConnect to a network of 7,500+ alumni worldwide.
Academic partners
Awards and rankings
Rated 4.8/5 across trusted platforms
Join an elite engineering ecosystem
Partner with Nebius Group, Big Tech engineers, and TripleTen alumni to scale your career.
Big Tech-reviewedLevel up, with proofGet your architecture audited by Big Tech experts. Graduate with references.
Active networkGet noticedAccess the hidden job market with direct pipelines to partner companies through 300+ daily tech opportunities.
Top-tier standardsPass the $200K+ barLearn the exact tradeoffs tested by OpenAI, Uber, and Stripe.
Featured in
How Education Is Changing In The AI Era
TripleTen grad Jeremy Rivera traded night shifts in a warehouse for a remote developer job.
Survey: How office workers are really using AI on the job
TripleTen is the overall best Software Engineering program for 2024.
Your complete Big Tech career accelerator
We’ll help you create tailored job search materials:
LinkedInResumePortfolioCover letter
Drill with Tier-1 hiring managers to hone your performance and fix blind spots.
Network of hiring partnersDaily job dropsRecruiter intros
target comp$200k+
Grow your leadership skills
Support beginner students in other TripleTen programs, mentor them, and build leadership experience—the kind that gets you into senior and staff-level roles.
Build a production-ready system that demonstrates your expertise
100% student-owned repoReal production faults
Modular portfolioProduction-grade AIBuild a versioned REST and gRPC gateway backed by PostgreSQL with read replication, Redis caching, and automated contract testing.
Provision production Kubernetes clusters using Terraform, automate deployments via CI/CD, and troubleshoot real incidents using live telemetry dashboards.
Architect an idempotent Kafka messaging pipeline built for chaos testing, complete with zero-trust auth, threat modeling, and automated security scanning gates.
Implement an LLM gateway, RAG pipelines over platform data, and autonomous agents with automated evaluation gates that block bad merges.
CapstoneThe architectural defense
Take your multi-service platform the last mile. You’ll implement a custom use case, take the system to a true production Definition of Done, and defend your technical trade-offs before an expert review panel.
Ready to level up?
Book a call to talk to a career mentor, get your questions answered, and reserve your spot.
How you’ll study
Download PDFFive projects → one production system35% theory / 65% hands-on
For engineers with 2+ years in production
AI-assistedProject first, theory when you need it.
The accelerator includes 250 hours of optional theory, there to support the projects you’re building.
Reviewed by engineers who build this in production.
And your final system will be evaluated by a staff-level engineer.
You keep everything.
Your own repositories keep the deployed services, the tests that prove they work, and your notes on each decision.
Project 01
“A public API that keeps its contract while the data model underneath keeps changing.”
Tools you use
RESTgRPCPostgreSQLRedis
Theory behind it
API and service architectureVersioning and deprecation strategyRelational data modelingRead replicationCache invalidationContract testing
What we hand you
A running PostgreSQL cluster, a partial spec, and consumers already depending on v1.
What you decide yourself
Where the read replicas and cache boundaries go — and what you version, deprecate, or break.
What the review checks
Whether v1 consumers still work after your change, and whether the contract tests prove it.
Project 02
“Deploys that stop being an event, and a cluster someone else can debug at 3 a.m.”
Tools you use
AWSDockerGrafanaTerraformKubernetes
Theory behind it
Containers and orchestrationInfrastructure as codeCI/CD pipelinesSLOs and error budgetsObservability
What we hand you
A production Kubernetes cluster, live telemetry dashboards, and a real incident to work.
What you decide yourself
Your deployment strategy, your rollback path, and what your alerts are allowed to page for.
What the review checks
How fast you can roll back, and whether your telemetry explains the incident.
Project 03
“A messaging pipeline that can be replayed without corrupting anything downstream.”
Tools you use
KafkaOAuth 2.0VaultThreat modeling
Theory behind it
Event-driven architectureExactly-once and idempotency patternsChaos engineeringZero-trust authenticationSecrets managementAutomated security scanning
What we hand you
A Kafka pipeline, a threat model to finish, and a chaos-testing harness pointed at your services.
What you decide yourself
Your idempotency keys, your zero-trust boundaries, and which security gates block a merge.
What the review checks
Whether the pipeline survives replay and chaos testing without corrupting state.
Project 04
“LLM features that stay affordable and don’t regress silently.”
Tools you use
RAGAgentic workflowsEvalsLLM gateway
Theory behind it
LLM gateway designRouting and fallback chainsRAG architecture and chunking strategyAgent orchestrationEvaluation harnesses
What we hand you
Platform data to retrieve over, a model budget, and an eval harness with no tests in it yet.
What you decide yourself
Your routing and fallback chain, your chunking strategy, and what a failing eval is allowed to stop.
What the review checks
Cost per request, retrieval quality you can measure, and evals that actually block a bad merge.
Capstone
“One system, documented well enough that a stranger can review the decisions behind it.”
Tools you use
C4 modelADRsScaling reviewRisk register
Theory behind it
Architecture documentation with the C4 modelDecision recordsTradeoff analysisTechnical communication
What we hand you
Everything you built, plus a staff-level engineer who wasn’t part of building it.
What you decide yourself
Which tradeoffs you defend, which you concede, and what you would do differently at 10× the traffic.
What the review checks
A 60-minute one-to-one review with a staff-level engineer who was not part of building it.
Full access to the TripleTen ecosystem
Access advanced ML, AI, and Cybersecurity modules on demand.
Built by the industry, for the industry
Backed by Nebius Group—a core technology partner of Meta, NVIDIA, and Microsoft. Across the group, 220+ senior engineers are building large-scale AI infrastructure, including global cloud platforms and hyperscale GPU clusters.
A global hiring network built for your career leap
We know exactly what the market demands. To bridge the talent gap, we rely on TripleTen’s proven training expertise and employer network, trusted by over 7,500 graduates across North America and Latin America.
300+Daily job opportunities on our board.
Flexible payment options
Upfront payment
Pay in a single installment, or up to 4 payments. You can withdraw with a 100% refund in your first 2 weeks.
upfront payment of$12,600
$12,600 in total
Tuition financing
Apply for financing with one of our partners, and learn if you are eligible in minutes. Soft credit check req’d.
Monthly payments starting at$350
$17,950 in total
TripleTen installments
$1,000 deposit. No credit check req’d, 0% interest. 6, 12, or 24-month plans available.
monthly payments starting at$792
$20,000 in totalAll options offer TripleTen’s money-back guarantee, valid for 10 months after graduation.
Our financial partners for tuition financing:
Random tutorials won’t teach you this
The patterns senior engineers pass down internally—how systems actually run in production at:
FAQ
AI Systems Engineering is a 22-week career acceleration program that takes you from feature-level work to system-level engineering. You learn to design, build, and scale production-grade systems, from distributed architectures to AI pipelines, and you graduate with a portfolio of four deployed, open-source products.
It is built for working engineers and tech-adjacent professionals who want to level up, not start over. If you already ship features and want to architect reliable, scalable systems, including production AI, this program meets you at that next step.
No. The program is designed to fit around full-time work. It runs about 22 weeks, so you can keep your current role while you build system-design skills and a portfolio that proves them.
It runs about 22 weeks: eight core modules, a one-week Operator UI Workshop, a five-week partner-company externship, and a capstone of four portfolio projects, ending in a final panel review. An AI In Practice thread runs through every module.
You cover system design, API and service architecture (REST, GraphQL, gRPC), data architecture, cloud infrastructure (Kubernetes, Terraform, GitOps, CI/CD), distributed systems and Kafka, security and compliance, AI systems design (LLM integration, RAG, model serving), and architecture documentation and technical leadership.
You build four standalone, open-source products: OpenMon, a self-hosted Datadog alternative; StreamFlow, a visual event-pipeline engine; CloudForge, an internal developer platform; and NeuralGate, an LLM gateway and evaluation layer.
You graduate with a portfolio that shows hiring managers what you can build. Engineers who work at companies like Meta, Amazon, and Google DeepMind review your projects as you go. Choose the format that suits your needs best: externships, hackathons, and technical competitions are available for AI Systems Engineering students.
You’ll learn from experienced engineers who work at top tech companies and receive personalized support through one-on-one mentoring sessions, office hours, code and project reviews, and career coaching. You’ll also have access to live lectures and webinars covering the latest trends and in-demand topics in tech. Our curriculum is regularly reviewed and updated to align with the skills employers are looking for today.
You finish with a portfolio of deployed, open-source systems you own, a real externship deliverable if you choose to take part in it, and a written panel assessment from industry reviewers. Together they are verified evidence that you can architect and ship production systems.