Системный аналитик

Description

🤓 TripleTen is an EdTech company that designs and runs tech career learning programs for the US and Latin American markets. We've been doing it for over five years, teaching complete beginners — people with no prior tech background — through cohort-based programs built on our own platform and curriculum, developed in partnership with Nebius AI. Our team is fully remote and globally distributed, and we serve a large, active student base across both regions.

We're building a program for experienced engineers transitioning into system architecture roles. We need AI Systems Engineering practitioners who can do more than just know their craft — they need to help build the curriculum and turn their expertise into content others can learn from.

This is a content authoring role. You'll start by shaping the program syllabus based on our existing draft, then move into producing lessons using AI-gen tools — following our content guidelines, reviewing the output, and fine-tuning it until it's accurate and teachable. An LX designer will review your content and send comments back; you'll iterate from there. The best candidates are people who are opinionated about what good systems engineering looks like and what engineers need to learn, can spot when something is oversimplified or wrong, and are comfortable working in a structured production process and keeping up with deadlines.

Your audience: experienced engineers looking to level up to system architecture roles. They will spot shallow content instantly.

Format: async-first, project-based, estimated 4-6 months. We expect you to be ready to around 15-20 hrs/week.

Areas of expertise

We will bring on three SMEs, each focused on one of the following areas.

SME A — System Foundations, API & Data

• Module 1 — Foundations of System Engineering (Architecture principles, OpenTelemetry, distributed systems)
• Module 2 — API & Service Architecture (REST, GraphQL, gRPC, service boundaries, DDD)
• Module 3 — Data Architecture & Storage Patterns (SQL/NoSQL, sharding, replication, Redis caching)

SME B — Cloud, Distributed Systems

• Module 4 — Cloud Infrastructure & Platform Engineering (Kubernetes, Terraform, GitOps, CI/CD, FinOps)
• Module 5 — Distributed Systems & Event-Driven Architecture (CAP theorem, event-driven, Kafka, alert routing)

SME C — Security, AI Systems & Architecture Leadership

• Module 6 — Security & Compliance Architecture (Zero-trust, threat modeling, compliance, secure CI/CD)
• Module 7 — AI Systems Design & Integration (LLM integration, RAG, ML reliability, AI governance)
• Module 8 — Architecture Documentation & Leadership (ADRs, C4 diagrams, technical communication)

Please indicate in your application which area fits you best. If you have overlapping skills, don’t hesitate to apply and indicate it in your application. Submit all resumes or CV's in English.

What you will do

• Review and shape the existing syllabus draft — validate topic sequencing, flag gaps or unrealistic scope, and make sure the content reflects how these systems are actually built in production.
• Define clear learning objectives per topic: what should a student be able to do after this lesson, not just know.
• Produce lessons using AI-gen tools following our content guidelines — then review, fact-check, and fine-tune the output until it's technically sound.
• Design hands-on projects and exercises that are realistic for a working engineer studying 20 hrs/week.
• Write supporting content: project instructions, rubrics, and reviewer guides.
• Respond to LX designer feedback and iterate on content — this is a back-and-forth process, not a one-time handoff.

Requirements

• 5+ years of real-world experience as a Senior Systems Engineer
• Has designed and shipped systems at scale in a real company — not just coursework or side projects
• Can explain why a decision was made, not just how it was implemented
• Strong technical communication skills — ability to explain complex topics clearly to learners
• Comfortable using AI tools for content drafting and iteration
• Strong English C1+ — content will be in English for a US-based audience
• Attention to detail, proactivity and ability to meet deadlines independently

What we can offer you

• Milestone-based payments tied to deliverables
• Fully remote work with flexible hours
• A digital office — we use modern tools (Notion, Miro, Slack, Figma) to keep collaboration smooth
• Professional trust and autonomy — no micromanaging
• The chance to see real impact: our metric is graduates landing jobs in tech
• Join a diverse, international, and close-knit team excited to work with you

Description

🤓 TripleTen is an EdTech company that designs and runs tech career learning programs for the US and Latin American markets. We've been doing it for over five years, teaching complete beginners — people with no prior tech background — through cohort-based programs built on our own platform and curriculum, developed in partnership with Nebius AI. Our team is fully remote and globally distributed, and we serve a large, active student base across both regions.

We're launching a program for experienced engineers transitioning into system architecture roles. The curriculum is being built by a team of senior authors. What we need now is the person who brings it to life for students: hosts the live sessions, reviews the work, runs the architecture review boards, and sets the technical bar for the people supporting alongside you.

This is a teaching and review role. You'll be the senior technical presence the cohort learns from week to week. You'll run live sessions and office hours, give real architectural feedback on student deliverables, and act as the escalation point for a team of supporting instructors who handle first-line questions. The best person for this is someone who has actually designed and shipped systems at scale, has opinions about what good architecture looks like, and can tell an experienced engineer why their design is wrong, not just whether it runs.

Your audience: experienced engineers leveling up to system architecture roles. Many already write production code daily. They will spot shallow feedback instantly, so the bar here is real seniority, not familiarity.

Format: Async-first with scheduled live sessions. Live sessions and office hours are timed around a US-based cohort. Estimated 15 to 25 hrs/week.

Areas of expertise

We will bring on three Experts, each focused on one of the following areas.

Expert A — System Foundations, API & Data

• Module 1 — Foundations of System Engineering (Architecture principles, OpenTelemetry, distributed systems)
• Module 2 — API & Service Architecture (REST, GraphQL, gRPC, service boundaries, DDD)
• Module 3 — Data Architecture & Storage Patterns (SQL/NoSQL, sharding, replication, Redis caching)

Expert B — Cloud, Distributed Systems

• Module 4 — Cloud Infrastructure & Platform Engineering (Kubernetes, Terraform, GitOps, CI/CD, FinOps)
• Module 5 — Distributed Systems & Event-Driven Architecture (CAP theorem, event-driven, Kafka, alert routing)

Expert C — Security, AI Systems & Architecture Leadership

• Module 6 — Security & Compliance Architecture (Zero-trust, threat modeling, compliance, secure CI/CD)
• Module 7 — AI Systems Design & Integration (LLM integration, RAG, ML reliability, AI governance)
• Module 8 — Architecture Documentation & Leadership (ADRs, C4 diagrams, technical communication)

Please indicate in your application which area fits you best. If you have overlapping skills, don’t hesitate to apply and indicate it in your application. Submit all resumes or CV's in English.

What you will do

• Mentor students through 1:1 video calls, Q&A sessions, and group events, helping them clarify concepts and navigate assignments.
• Host webinars and workshops on program topics, industry insights, and extracurricular skills.
• Review and assess student projects, providing clear, constructive feedback aligned with program standards.
• Engage with students on the Hub through written guidance, ongoing communication, and curated learning resources.
• Share professional expertise to equip students with practical skills and career strategies.
• Foster a collaborative learning environment that builds confidence, encourages interaction, and supports student growth.
• Conduct Mock Interviews providing constructive feedback on both their technical responses and interview skills.
• Record instructional videos for the curriculum, clearly explaining key concepts, methodologies, and course topics to support student learning.

Requirements

• 5+ years as a senior software/systems architect, platform engineer, or senior backend/infrastructure engineer
• Has designed and shipped systems at scale in a real company, not just coursework or side projects
• Depth across the architectural spine: system design, API and service architecture, cloud and infrastructure (AWS, Kubernetes, Terraform, CI/CD), and distributed systems
• Can explain why a decision was made, not just how it was implemented, and can diagnose and critique someone else's architecture live
• Strong technical communication: comfortable leading a live session and writing clear, specific review feedback
• Strong English C1+ — instruction and review are in English for a US-based audience
• Comfortable using AI tools in day-to-day technical work

What we can offer you

• Milestone-based payments tied to deliverables
• Fully remote work with flexible hours
• A digital office — we use modern tools (Notion, Miro, Slack, Figma) to keep collaboration smooth
• Professional trust and autonomy — no micromanaging
• The chance to see real impact: our metric is graduates landing jobs in tech
• Join a diverse, international, and close-knit team excited to work with you

Description

🤓 TripleTen is a career learning platform for tech professionals and complete beginners ready to move into higher-paying tech and AI roles. We launched in 2020, we run programs across the US and Latin America, and 7,500+ people worldwide have completed one. Our team is fully remote and globally distributed.

In 2026 we opened a second tier of programs for people already working in tech: AI Systems Engineering, AI & Machine Learning, and Forward Deployed Engineering. Same platform, different bar.

We're launching three advanced engineering programs for working mid/senior engineers, and we're looking for a small number of Industry Experts to set the technical bar in each of them.

This is not a teaching or content-authoring role. The curriculum is built by a separate team of senior authors. What we need from you is judgment: the kind of call a Staff or Principal engineer makes when they look at a design and know, in thirty seconds, that the service split is wrong, the eval is measuring the wrong thing, or the scope will not survive contact with a client.

Our students design and defend real systems. Your role is to challenge those decisions the way you'd challenge a peer's — and to be the name that tells an experienced engineer this program is worth their time.

What you will do

Each program is a chain of five production-level projects, and every project ends in a live defense.

• Sit on final project defenses. Review a deployed system, a distributed-systems capstone, an agentic architecture, or a client-facing delivery package against the rubric — then run the defense and give structured, senior-level critique.
• Chair mock review boards and executive-panel presentations. Architecture review boards, model and system reviews, exec go/no-go presentations, depending on the program.
• Host one or two live sessions a month on the design and decision layer of your domain: where systems split, how they fail, which tradeoff to make and why.
• Set the technical standard for the instructors running weekly delivery, and act as their escalation point on the hard design calls.

You are not on the hook for weekly coverage, office hours rotations, or first-line questions. A separate team handles that.

Who you'd be reviewing

Working engineers and tech professionals, not complete beginners. Middle or senior developers, platform and data engineers, network and infrastructure people, security and incident-response specialists. A few are between jobs and moving fast. Most have hit a ceiling where they are and want the next step: designing and owning production AI systems instead of shipping features around them.

Most of them study around a full-time job, they opened your GitHub before the syllabus, and they can tell rehearsed feedback from the real thing.

Requirements

• 8+ years of professional engineering experience, currently at senior/staff/principal level or equivalent (Staff/Principal Engineer, Senior/Staff ML Engineer, Solutions Architect, Forward Deployed Engineer, technical lead).
• You've shipped systems that run in production at real scale, as an employee in an engineering role — not coursework, not side projects, not a slide deck about someone else's platform.
• You can explain why a decision was made, not just how it was implemented — and diagnose and critique someone else's architecture live, on a call, without preparation.
• A public technical footprint: GitHub, conference talks, a book or O'Reilly/Manning title, a technical blog, open-source work, or documented mentorship.
• Strong English (C1+). Sessions and written reviews are in English for a US-based audience.
• Time zone: Americas strongly preferred (US / Canada / LatAm). Defenses are booked in advance, so some flexibility exists — but sessions land in US afternoon and evening hours.
• Comfortable using AI tools in day-to-day technical work.

Domain depth — one of three tracks

You don't need all three. Tell us which one is yours.

AI/ML Engineering. Agentic systems and orchestration (LangChain, LangGraph, CrewAI, ADK), agent reliability and guardrails, MCP; LLM evals — eval harnesses, LLM-as-judge, hallucination metrics; applied fine-tuning (SFT/LoRA/PEFT); LLM observability, A/B experiment design, model serving and inference cost.

AI Systems Engineering. System and API design, service architecture, cloud and infrastructure (AWS, Kubernetes, Terraform, CI/CD), distributed systems, observability and incident response — plus LLM-powered systems in production: RAG, model serving, fallback paths, cost control.

Forward Deployed Engineering. End-to-end ownership of deployments in real client or enterprise environments: discovery and scoping under ambiguity, stakeholder management without formal authority, integration with enterprise systems, rollout and adoption — on top of LLM and agent systems in production, RAG over enterprise data, and APIs/integrations.

Nice to have

• You've already run technical sessions in some form: internal tech talks, conference workshops, engineer onboarding, or mentoring.
• Hands-on ownership of an eval or observability stack in production, not just usage of one.
• Experience being the primary technical resource embedded with a customer team (for the FDE track).

What we can offer you

• Your name and profile featured as an Industry Expert on the program page.
• A network of engineers from other companies. The other instructors/industry experts come from engineering teams US engineers recognize, and you'll be working alongside them.
• First look at senior talent. You watch experienced engineers defend real systems under pressure, so you leave the cohort knowing who you'd hire.
• Personal brand, with proof behind it. Your profile on the program page, plus an Industry Expert line for your own bio and talks. It's also the kind of external technical credit that counts in a promotion packet or an O-1 petition.
• A genuinely small commitment. 4–10 hours a month, slots booked about two weeks ahead, pausable at any time.
• Hourly payment, negotiable depending on experience, track, and scope.
• Fully remote, with a small international team and no micromanaging.