Present all four capstone projects to a review panel. Receive architecture-level feedback, defend your design decisions, and walk away with a professional evaluation you can share directly with employers.
Portfolio presentation to reviewers
Architecture-level feedback
Shareable final evaluation
Ready to level up?
Book a call with an admissions advisor to get your questions answered and reserve your spot.
Every module maps to specific job requirements
Job requirements
Staff/Senior Software Engineer, Inference
$300,000–$485,000
They ask for:
Load balancing and traffic management systems.
LLM inference optimization, batching, and caching strategies.
Kubernetes and cloud infrastructure deployments.
The TripleTen solution
How you’ll master this:
Module 7 & 3
Master architecture for LLM gateways (smart routing, fallback chains) and advanced caching patterns.
Capstone Project: Neural Gate
Architect a production-grade AI routing layer that manages request distribution and cost budgets.
Module 4
Get hands-on with container orchestration using Kubernetes and infrastructure as code via Terraform.
Job requirements
Forward Deployed Engineer (FDE)
$162,000 – $280,000 + Equity
They ask for:
Manage end-to-end deployments of frontier models.
Make trade-offs between scope, speed, and quality.
Communicate clearly with teams and stakeholders.
The TripleTen solution
How you’ll master this:
Module 8
Learn to translate technical tradeoffs into business language and defend decisions using Architecture Decision Records (ADRs).
Real-world case study
Amazon Architecture Decision Process: Manage long-term system evolution across large engineering teams.
Job requirements
Senior Software Engineer, Amazon AI at Work
$168,100–$227,400
They ask for:
Build the next generation of AI agents.
Architect and implement robust systems in problem areas.
Engage stakeholders in thoughtful discussions.
The TripleTen solution
How you’ll master this:
Module 7
Master agentic routing and RAG architecture patterns to build scalable AI systems.
Module 8
Move from coding to architecture leadership by learning to build consensus through formal technical writing.
Real-world case study
Amazon Architecture Decision Process: Manage long-term system evolution across large engineering teams.
Job requirements
Staff GenAI Backend Engineer
$204,000–$255,000
They ask for:
Expertise in RAG patterns, memory routing, and agent planning.
Optimize backend workflows for a scalable, flexible architecture.
Prior experience building developer infrastructure and tooling.
The TripleTen solution
How you’ll master this:
Module 7
Deep dive into RAG architectures, chunking strategies, and vector stores.
Projects: StreamFlow & CloudForge
Build an event-driven workflow automation engine and your own Internal Developer Platform (IDP).
Real-world case study
Airbnb Infrastructure Evolution: Analyze their monolith-to-SOA migration driven by infrastructure scaling costs.
Job requirements
Full-Stack Engineer 5, AI Platform
$388,000–$558,000
They ask for:
Build powerful internal tools and platforms for ML practitioners.
Improve observability, detect anomalies, and monitor health.
Assess tradeoffs of refactoring, rebuilding, or buying.
The TripleTen solution
How you’ll master this:
Operator UI Workshop
Integrate AI metrics into Grafana, Langfuse, and Backstage. Learn the 'Build vs. Adopt' decision framework.
Project: OpenMon
Design a distributed monitoring platform that ingests high-throughput metrics and routes alerts.
Real-world case study
Netflix Microservices Evolution & Event-Driven Platform: Service decomposition and resilience engineering.
Job requirements
Senior Fullstack Engineer, Generative UI
$133,000 – $190,000
They ask for:
Integrate LLM coding agents and evaluation loops.
Manage distributed systems using Docker and Kubernetes.
Write clearly and create thoughtful documentation.
The TripleTen solution
How you’ll master this:
Project: NeuralGate
Build an LLM gateway with automated evaluation pipelines
Module 4
Master Docker, Kubernetes orchestration, and GitOps workflows.
Module 8
Master technical writing with C4 diagrams and ADRs.
Real-world case study
Spotify Kubernetes Platform: Container orchestration at scale and service reliability.
Job requirements
Software Engineer, Data & AI
Top-tier Big Tech compensation
They ask for:
4+ years maintaining large scale distributed systems.
Design, build, and maintain APIs across engineering teams.
Create elegant abstractions for complex financial patterns.
The TripleTen solution
How you’ll master this:
Module 2 & 5
Deep dive into fault tolerance, distributed transactions, idempotency, and designing highly scalable REST/GraphQL APIs.
Real-world case study
Stripe Fraud Detection Architecture: Combine real-time ML inference with fallback logic and human review queues.
Job requirements
Senior Software Engineer, AI
$202,000–$224,000
They ask for:
Microservice designs, gRPC or REST API development.
Experience with distributed systems like Kafka and GenAI.
Strong writing skills for design and architecture.
The TripleTen solution
How you’ll master this:
Module 2 & 5
Master gRPC, REST, and message brokers like Kafka to build highly scalable microservices.
Module 8
Develop top-tier technical writing skills by learning to produce Architecture Decision Records (ADRs) to communicate system designs.
Real-world case study
Uber Real-Time Dispatch System: Real-time event processing and geospatial indexing at scale.
Modern software architecture: monolithic vs microservices, layered/service-oriented patterns. Scalability, reliability, and observability using OpenTelemetry.
AI In Practice: Using AI to analyze architecture risks and bottlenecks.
Service communication at scale: REST, GraphQL, gRPC. API versioning, service discovery, and domain-driven design (DDD). Service boundaries and decomposition strategies.
AI In Practice: AI-assisted API design review and peer feedback comparison.
Shopify GraphQL API architecture · Google gRPC for internal microservices
Data architecture & storage patterns
PostgreSQL
Redis
Cassandra
Elasticsearch
SQL vs NoSQL tradeoffs, sharding, and replication. Caching patterns (Redis), invalidation strategies, and rate limiting for high-throughput systems.
AI In Practice: Proactive schema optimization and N+1 query risk analysis using AI.
LinkedIn distributed data architecture—real-time analytics pipelines and replication
Cloud infrastructure & platform engineering
Kubernetes
Terraform
ArgoCD
AWS
Kubernetes orchestration, service mesh, and Infrastructure as Code (Terraform). GitOps (ArgoCD), CI/CD pipelines, cost engineering, and FinOps culture.
AI In Practice: AI-assisted Terraform generation and cloud cost optimization analysis.
Spotify Kubernetes platform—container orchestration and deployment automation
Distributed systems & event-driven architecture
Kafka
RabbitMQ
Redis
NATS
CAP theorem, consistency models, and distributed transactions. Message queues, pub/sub, and event streaming with Kafka. Alert routing and rules engine patterns.
AI In Practice: Using AI for root cause hypothesis generation during system failures.
Uber real-time dispatch system · Netflix event-driven platform
Security & compliance architecture
Vault
OAuth 2.0
mTLS
OWASP
Zero-trust architecture, threat modeling (STRIDE), and secure design. Identity patterns (OAuth 2.0/OIDC), secrets management, and compliance by design.
AI In Practice: Security architect roleplay for threat modeling and attack vector analysis.
Cloudflare Zero-Trust Architecture—transition from VPN to zero-trust
AI systems design & integration
Python
FastAPI
Langfuse
Vector DBs
LLM integration, RAG architecture (vector stores, chunking), and LLM gateway design. AI reliability patterns, fallback chains, and governance frameworks.
AI In Practice: Building AI-native evaluation pipelines and regression test suites.
Stripe fraud detection—combining real-time ML inference with rule-based logic
Architecture documentation & leadership
C4 Model
ADR
Grafana
Backstage
C4 model, architecture diagrams, and ADRs (Architecture Decision Records). Tradeoff analysis and technical communication. Includes 1-week Operator UI workshop.
AI In Practice: AI as a first-draft collaborator for ADRs and technical proposals.
Amazon architecture decision process—documenting tradeoffs at scale
Capstone projects
K8s
Kafka
Terraform
Python/Go
5-week externship on a real engineering team. 12 weeks building 4 open-source products: OpenMon (Monitoring), StreamFlow (Pipelines), CloudForge (IDP), and NeuralGate (AI Gateway).
Architecture Review Panel—portfolio defense for senior industry experts
Full access to the TripleTen ecosystem
Access advanced ML, AI, and Cybersecurity modules on demand.
Advanced ML & AIOffensive CybersecurityDefensive SecOps
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.
Random tutorials won't teach you this
The patterns senior engineers pass down internally—how systems actually run in production at:
AI Systems Engineering is a 9-month 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.
Who is the AI Systems Engineering program for?
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.
Do I need to quit my job to take the program?
No. The program uses a flexible learning schedule designed to fit around full-time work. It runs about 40 weeks, so you can keep your current role while you build system-design skills and a portfolio that proves them.
How long is the program and how is it structured?
It runs about 9 months (roughly 40 weeks): eight core modules, a one-week Operator UI Workshop, a five-week partner-company externship, and a 12-week capstone of four portfolio projects, ending in a final panel review. An AI In Practice thread runs through every module.
What will I learn in the AI Systems Engineering curriculum?
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.
What projects will I build in the program?
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.
Does the program include real work experience?
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 suites your needs best: externships, hackathons, and technical competitions are available for AI Systems Engineering students.
What kind of support and guidance can I expect during my studies?
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.
What portfolio will I be able to show employers after I finish?
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.
Airbnb
Uber
Anthropic
Open AI
Prometheus
Grafana
Kafka
Node.js / Go
Docker
Terraform
PostgreSQL
Redis
React Flow
Kubernetes
ArgoCD
GitHub Actions
Python
FastAPI
OpenTelemetry
gRPC
GraphQL
Protocol Buffers
OpenAI
Cassandra
Elasticsearch
AWS
RabbitMQ
NATS
Vault
OAuth 2.0
mTLS
OWASP
Python
Langfuse
Vector DBs
C4 Model
Backstage
