AI Systems Engineering

The program is for engineers with hands-on production experience who want to move from using AI tools to building AI systems:

Backend, full-stack, frontend, and mobile engineers who build APIs and business logic and want distributed systems and AI skills

DevOps, SRE, cloud, and platform engineers who run pipelines and uptime and need to support AI workloads

Engineers with a strong tech base but under 2 years in production, such as junior engineers, QA and IT specialists, analysts, or CS graduates

We recommend 2+ years of production work as a guide rather than a strict cut-off: recent hands-on work matters more than total years. Admission starts with a skills audit.

After the skills audit, an advisor reviews your production experience on a call and walks you through the syllabus to see how it compares with your current skills. If you already know part of the material, you'll see which theory lessons you can skip.

No. The program is designed for people working full time. There are no fixed class times, and all live sessions run in the evenings and on weekends. Plan for 10 hours a week on projects, or up to 15 if you take all the theory. At fewer hours a week, the program takes longer.

At 10 hours a week, the program takes 22 weeks on projects alone, or 33 weeks if you take all the theory. You'll complete 220 hours of project work across five projects that build on each other, plus up to 110 hours of optional theory, so you can skip topics you already know. Each project includes a 30-minute check-in with your instructor at about 70% done and a 60-minute final project review with a working engineer.

You'll learn to run, extend, and secure a production AI system:

Observability and capacity: OpenTelemetry, Jaeger, Prometheus, Grafana, load testing with Locust, and capacity math on PostgreSQL and Redis

Retrieval and service architecture: RAG with pgvector and hybrid search, REST versioning, idempotency, migrations, and ports and adapters

Reliability: rollout and rollback, timeouts and retries, provider fallback, queues and dead-letter queues, SLOs, alerting, and CI gates

Security and governance: STRIDE threat modeling, OIDC and RBAC, output guardrails, PII redaction, audit logging, and secrets management

Agentic AI: LangGraph agents with MCP tools, human-in-the-loop checkpoints, LLM evaluation, and delivery to AWS S3, SQS, and Secrets Manager

Project code is Python 3.12 with FastAPI. Kubernetes, Kafka, and GPU serving are covered at the decision level.

You join the platform team at TripleTen Medical, a fictional company running Coldline, a cold-chain logistics platform. Across five projects, you'll diagnose it for 10x growth, add authorization-aware retrieval, keep it running through provider and queue failures, secure it with guardrails and governance, and finish with an autonomous agent that proposes resolutions and asks a human before it acts. You'll connect the capstone to real AWS services and present it on Final Demo Day.

Yes. You'll work in a running codebase, submit pull requests, and pass automated tests and an instructor's code review before each final project review. Each project starts from a reference solution of the previous one, with acceptance criteria known upfront.

From day one, you'll meet your instructor for a 30-minute 1:1 every week. Instructors are working engineers with backgrounds at AMD, Microsoft, and Amazon. You also get written feedback on every project, a project space where instructors answer between sessions, and group office hours.

Your career coach joins after your first project and stays with you for up to 12 months. After the program, you keep lifelong access to program updates.

You'll finish with one production AI system on GitHub, built across five projects and reviewed live by working engineers, plus written feedback on every project. Each reviewed project becomes a case study you can walk interviewers through in system design rounds.

Yes. DevOps, SRE, and infrastructure engineers already run the pipelines AI systems depend on. The program adds observability for AI request paths, reliability under model-provider failure, security and governance for AI workflows, and agent delivery to cloud services. Graduates target roles such as AI Platform Engineer and AI Infrastructure Engineer.

Yes. Each final project review works like a system design round. Career support adds up to 10 live mock interviews (screening, technical, recruiter, and behavioral) with instructors and your coach, a plan after each one, unlimited AI mock interview practice, and debriefs of your real interviews. You practice explaining your own retrieval, evaluation, and agent design choices, the topics LLM interview rounds ask about.

No. The hiring decision is always the employer's, so no program can promise a job. What we give you is better chances: the skills employers are hiring for, production projects reviewed by working engineers, and a career coach who works with you through your job search. Landing the role is up to you.

Yes. Cancellation is free during the first 14 days. You can start the program, explore the platform, and meet your instructor, then leave at no cost if it isn't the right fit.

Yes. We accept employer-funded tuition in three ways:

Your company pays directly: we invoice your company, and it pays by bank transfer or ACH. If the invoice is paid before your cohort starts, your company pays the upfront price.

Your company pays part: your company pays its share by invoice, and you pay the rest with any payment plan. Your first payment holds your seat.

Your company reimburses you: you pay with any plan, and your company pays you back. We provide the paperwork your HR team needs.

To make the request easier, we can send you:

A request template for your manager or HR

The program description and syllabus

An enrollment letter with your cohort dates

An invoice in your name or your company’s

A certificate and transcript once you finish the program

No. You need solid programming experience in a modern language, not Python specifically. The program includes an optional Python theory block for engineers who work in other languages.

TripleTen is licensed by the Wyoming Department of Education and is registered under California law as an out-of-state institution providing private postsecondary education. You finish with a completion certificate and a portfolio of reviewed production work.