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PFE Book 2027 · Internship program

Build real value. Go live.

22 final-year projects on real UPTech products.

3–6 monthsHybridFrom February 2027

Apply now Explore the projects

Applications close on 25 November 2026, 23:59 (Tunis time)

Applications are closed. Thank you to everyone who applied.

Explore the book with your AI. No PDF needed: download the whole PFE Book as one Markdown file and ask your AI assistant which project fits you.

Download the book (.md)
Prompt ideas
  • Here is the UPTech PFE Book 2027 and my CV. Which 3 projects fit my profile best? Explain why, and what I should learn first.
  • Explain project REF-... in simple words: the problem, what I would build, and how the result is evaluated.
  • Make me a 4-week preparation plan for project REF-..., based on its key tasks and tools.
  • Ask me 5 interview questions about project REF-..., one at a time, and give me feedback on my answers.

Use AI to understand and prepare; write your application answers yourself.

About UPTech

Founded in 2019, UPTech helps businesses work smarter with integrated technology. Our expertise covers software development, ERP integration, cloud and DevOps, RFID systems and IT operations management. Our mission: turn operational complexity into simplicity. Our vision: predictive, data-driven operations that create real value for our clients.

What you get

  • Weekly mentoring by experts, on a real UPTech product or platform
  • A project that goes live and is measured on real usage
  • Hybrid work: remote, and the office when your project needs it
  • Our reference library, our self-hosted lab and an AI onboarding in week 1
  • A possible end-of-internship bonus: training, certification or subscription

The internship is unpaid.

What we expect

  • Serious commitment, from day one
  • Documentation first: what counts is what is written
  • Your report in LaTeX, from week 1
  • Less is more, and the process we have followed fully
  • Ask, ask, ask: never stay stuck
  • Honesty about what you know and how you use AI
How we work
  1. Weeks 1–2Charter signed
  2. Week 4First version in production
  3. Week 8Pilot with real users
  4. Week 12Production go-live
  5. Weeks 13–17Measure and hand over
  • 8 two-week sprints for a 4-month internship; 3-month and 5–6-month internships get a shorter or longer plan.
  • A project charter in week 2 fixes the scope and the Definition of Done; the expected result does not change.
  • Delivery every sprint in your GitLab repository, with a short review; your LaTeX report grows with the project.
  • Alone or in a pair, with tools as a starting proposal, on our 100 % self-hosted platform (Docker locally, Kubernetes for staging and production).

Durations, sprint calendar and delivery rules: in the PFE Book (.md).

Our 2027 projects

Pick one project and note its REF code: you choose it in the form.

UPTech AIOps Lab — Integrated IT Operations Platform 6

REF-AIOPS01

Knowledge-grounded incident diagnosis (RAG evaluation)

EngineeringMaster

When an incident is opened, technicians lose time searching old tickets and wiki pages. Build and evaluate an assistant that retrieves the most useful past tickets, knowledge articles and service-topology context, and proposes a diagnosis with citations. You will compare plain RAG with a hybrid retrieval that also uses topology and article reliability, on a labelled set of incidents.

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Key tasks

  • hybrid retrieval (BM25 + embeddings + reranker)
  • topology-aware scoring
  • LLM diagnosis with citation check
  • evaluation set and benchmark (Recall@k, MRR, grounded accuracy, faithfulness, latency, cost)

Tools (preliminary proposal)

Python, PostgreSQL/pgvector, sentence-transformers, Ollama (local LLMs), ITSM, Wiki.js · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: AI / Data Science / Software Engineering. Interest in NLP and evaluation.

REF-AIOPS02

Risk-gated remediation: when may the AI act alone?

Engineering

AI can propose a fix for an incident, but executing it is risky. Build a remediation engine with three response modes: apply a low-risk, reversible action automatically; prepare a change (a merge request with the playbook) for human approval; or comment with referenced alternatives. The choice is made by a calibrated, risk-aware rule using confidence, blast radius on the service topology and reversibility. Automatic actions run only in a sandbox cluster; on production alerts the engine works in advisory mode (merge requests and comments).

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Key tasks

  • action catalogue (Ansible playbooks + rollback)
  • risk score and decision rule
  • "change as pull request" workflow in GitLab and ITSM
  • sandbox fault injection
  • safety metrics

Tools (preliminary proposal)

Python, Ansible/AWX, GitLab CI, ITSM, Prometheus, Kubernetes (sandbox) · Docker for local tests, Kubernetes for staging and production

Profile

Engineering: DevOps / Cloud / Software Engineering, with an interest in AI safety and automation.

REF-AIOPS03

Knowledge capture loop: every solved ticket teaches the service desk

EngineeringMaster

Most solutions stay in technicians' heads or in closed tickets. Build a knowledge loop inside the service desk: when a ticket is solved, an assistant drafts a knowledge article from the resolution, the technician approves or edits it, and each suggested article later receives feedback ("did it help?"). Articles that age or fail lose reliability. Dashboards show how knowledge reuse changes resolution time.

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Key tasks

  • ITSM plugin for capture and feedback
  • AI article drafting with human approval
  • reliability and freshness scores
  • knowledge dashboards (reuse rate, freshness, MTTR)
  • pilot with the UPTech support team

Tools (preliminary proposal)

ITSM platform (plugin or API), Python/FastAPI, Wiki.js, PostgreSQL, Grafana, Keycloak SSO, local LLM · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: Software Engineering / Information Systems; ITIL knowledge is a plus.

REF-AIOPS04

Streaming observability lakehouse with data contracts and freshness SLOs

EngineeringMaster

AI for IT operations is only as good as its data. Build the data backbone of the UPTech operations platform: metrics, logs, traces, tickets and the service topology flow through RabbitMQ into a lakehouse (bronze/silver/gold), with data contracts, quality checks and freshness objectives. Then measure how stale or missing data degrades incident detection and diagnosis.

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Key tasks

  • OpenTelemetry collection
  • RabbitMQ Streams ingestion
  • lakehouse (Iceberg/Delta on the self-hosted S3 object store)
  • topology from the ITSM CMDB
  • data contracts and freshness SLOs
  • degradation experiments

Tools (preliminary proposal)

OpenTelemetry, Prometheus, Loki, RabbitMQ Streams, Python consumers, Spark (batch), Iceberg/Delta, Ceph RGW or Garage (S3), dbt, Great Expectations/Soda, Grafana · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: Data Engineering / Big Data / Cloud.

REF-AIOPS05

IT-operations benchmark: incident prediction and root-cause ranking

EngineeringMaster

Build UPTech's reference benchmark of IT-operations data (metrics, logs, tickets, service topology, injected faults) and use it to evaluate and choose the detection and root-cause localisation models of the UPTech operations platform, with rigorous splits and baselines. The benchmark runs as an internal service, so every new model is compared on the same data before it goes to production.

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Key tasks

  • fault-scenario simulator
  • dataset documentation (datasheet)
  • time- and host-grouped splits
  • detectors (Isolation Forest, Random Forest, LightGBM, XGBoost)
  • topology-aware root-cause ranking (personalised PageRank)

Tools (preliminary proposal)

Python, pandas, scikit-learn, XGBoost/LightGBM, networkx, MLflow · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: Data Science / AI / Applied Mathematics.

REF-AIOPS06

Securing AI operations agents: telemetry guardrails, red-teaming and LLM observability

EngineeringMaster

AI agents that read logs and tickets can be manipulated: an attacker can write instructions into a log line or a ticket. Build the security layer of the UPTech AI operations assistant: an "untrusted telemetry boundary" that masks dangerous fields, a red-team test suite of poisoned logs and tickets, and LLM observability (traces, cost, latency, failures). Measure how often attacks succeed with and without the guardrails.

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Key tasks

  • threat model (OWASP Top 10 for LLM apps)
  • input boundary and output policy checks
  • attack suite (prompt injection in telemetry)
  • LLM tracing and cost dashboards
  • fail-safe behaviour when the LLM is down

Tools (preliminary proposal)

Python, Ollama, Langfuse (self-hosted)/OpenTelemetry, Garak or PyRIT, Wazuh, Grafana · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: Cybersecurity / AI / Software Engineering.

AI Engineering Projects 2

REF-AISDLC01

AI Project Manager: an AI-driven software lifecycle and release-batch manager

EngineeringMaster

UPTech delivers many interconnected microservices (ThePortal and other products) through one GitLab and Argo CD pipeline. UPTech already has a project-management framework (hybrid PMBOK + Agile, with governance, registers and cost tracking on OpenProject and GitLab), but keeping it up to date, reviewing and releasing are still manual. Design and implement UPTech's AI-driven SDLC on top of this framework: an AI project manager that helps at every stage, from turning requirements into user stories with acceptance criteria to merge-request summaries and review checklists, release notes, sprint and status reports, and project registers (risks, issues, changes) kept up to date from GitLab and OpenProject. Its core is a release-batch manager: it maps dependencies between services, groups ready changes into safe production batches, scores the risk of each change and prepares the promotion from staging to production. Humans approve every merge and every deployment.

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Key tasks

  • AI-augmented SDLC process (stages, roles, gates)
  • GitLab assistant (story refinement, MR summary and review checklist, release notes)
  • project registers and weekly status reports from GitLab and OpenProject
  • service dependency map
  • release-batch planning and change risk scoring
  • promotion staging → production through GitOps with approval gates
  • delivery dashboard (DORA metrics, lead time, AI usage)

Tools (preliminary proposal)

Python, FastAPI, OpenProject (API), GitLab API and webhooks, GitLab CI, Argo CD, Kustomize, RabbitMQ, PostgreSQL, local LLM (self-hosted, CPU), Langfuse, Grafana · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: Software Engineering / DevOps / AI. Interest in software delivery, LLM applications and process design.

REF-AITEST01

AI Tester: an AI-powered software testing framework

EngineeringMaster

UPTech's microservices are tested with JUnit, Postman, SonarQube and k6, but writing and maintaining tests takes time and coverage is uneven. Build an AI-powered testing framework that plugs into GitLab CI: it generates unit tests for Spring Boot services and runs, checks and fixes them before proposing them; it generates and maintains the Postman collections run by Newman from the OpenAPI contracts, and builds end-to-end UI tests from user stories, with locators that repair themselves. It also selects the tests affected by each merge request, flags flaky tests and handles test reports end to end: every run is collected, published on the merge request, kept with its history and trends, triaged with AI, turned into defects when confirmed, and summarised in a test report for each release. Every generated test is proposed as a merge request and reviewed by a developer, and test quality is measured with mutation testing, not only coverage.

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Key tasks

  • LLM unit-test generator (generate → compile → run → fix loop)
  • Newman API tests (Postman collections) generated from OpenAPI
  • Playwright E2E tests from user stories with self-healing locators
  • test-impact selection per merge request
  • flaky-test detection
  • end-to-end test-report management (collection, history, triage, defects, release test report)
  • quality dashboard (coverage, mutation score, CI time)

Tools (preliminary proposal)

Java, JUnit 5, Mockito, PIT, Newman (Postman collections), Playwright, Python, local LLM (self-hosted, CPU), GitLab CI, SonarQube, Allure · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: Software Engineering / Quality Assurance / AI. Interest in testing, Java and LLM applications.

Development Projects 5

REF-DEV01

ThePortal: field-service app for technicians (event-ready for ERP)

EngineeringMaster

ThePortal is UPTech's microservices suite for HR, logistics, sales, after-sales and call-center processes. Its After-Sales (SAV) microservice already handles complaints, plans interventions and assigns technicians. In 2027 we take it to the field: a mobile app for technicians (interventions, scheduling, offline mode, photos and signatures) with intervention events published so that client ERPs (Divalto, Sage) can be connected next. You will work on a real product used by clients, with code review and CI/CD.

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Key tasks

  • extend the SAV intervention service (scheduling, SLA, reports)
  • Flutter mobile app with offline sync
  • intervention events on RabbitMQ (outbox pattern), ready for ERP integration
  • Keycloak SSO and roles
  • automated tests and performance tests

Tools (preliminary proposal)

Java, Spring Boot, Spring Cloud (Gateway, Eureka), Keycloak, Angular, Flutter, MySQL, RabbitMQ, GitLab CI/CD · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: Software Engineering, full-stack (Spring Boot + Flutter).

REF-DEV02

Octo: a proactive, trusted knowledge hub for any organisation

EngineeringMaster

Every company has knowledge spread across people, tickets and documents, and no way to know what is still true. Octo is a self-hosted knowledge hub for any organisation, from a 20-person SME to a corporate group. Employees ask questions on general topics; critical topics are visible by role; engineers and auditors explore a topic before a mission. Anyone, newcomers included, can contribute; experts confirm, update or dispute. Each piece of knowledge gets a trust score from the evidence behind it. Octo is proactive: it detects contradictions, contested or outdated knowledge and unanswered questions, and asks the right knowledge ambassador for a judgment or a clarification. Part of the project is research: you design and compare trust and confusion-detection models. This product belongs to its founders and is developed at UPTech: an IP and confidentiality agreement is signed at the start.

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Key tasks

  • configurable organisation profile (domains, access levels, roles, ambassadors)
  • contribution and review workflow, fair to newcomers
  • trust scoring engine (Bayesian, expert-weighted, time decay, independence)
  • confusion detection (contradiction, contested, stale, gaps) with ambassador cases
  • role-based AI answers with citations and trust

Tools (preliminary proposal)

Python, FastAPI, PostgreSQL/pgvector, local LLM and NLI models (self-hosted), Keycloak, Angular or React, Langfuse, GitLab CI · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: Software Engineering / AI / Data Science. Interest in knowledge management, probability and NLP.

REF-DEV03

Qima: a mentoring and internship platform that builds value

LicenceEngineering

Qima (قيمة, "value") is a platform where everyone builds value together. You will build it: it connects mentors, interns, companies and training centres. Mentors publish topics and follow interns sprint by sprint; interns deliver their work linked to GitLab and get feedback; companies post real projects and follow their interns. UPTech will use it first to run its own PFE programme (topics, applications, weekly follow-up, evaluation grids). This product belongs to its founders and is developed at UPTech: an IP and confidentiality agreement is signed at the start.

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Key tasks

  • roles and profiles
  • topics and applications
  • selection grids
  • intern work plan, tasks and reviews
  • sprint deliveries linked to GitLab
  • evaluation grids and dashboards

Tools (preliminary proposal)

Spring Boot or NestJS, Angular or Next.js, PostgreSQL, Keycloak, GitLab API · Docker for local tests, Kubernetes for staging and production

Profile

Licence or Engineering: Software Development, full-stack.

REF-DEV04

Offline-first point of sale (POS) for small and medium stores, connected to ThePortal

EngineeringMaster

Small and medium stores need a cash register that never stops selling, even when the internet is down. Build an offline-first point-of-sale application: cashiers scan products, take payments, print receipts, handle returns and close their shift without any network. Every operation is stored safely on the device and synchronised automatically with the server as soon as it can be reached, with no lost or duplicated sale. Catalogue, prices and stock come from ThePortal, UPTech's business platform, and sales flow back to it. You will deploy it in a real client store in Tunisia.

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Key tasks

  • cashier app (scan, cart, payments, receipts, returns, shift close)
  • local database and operation outbox
  • sync protocol (idempotent, delta catalogue, conflict rules)
  • offline login and data encryption
  • receipt printer and barcode scanner
  • small back-office
  • chaos testing of network failures

Tools (preliminary proposal)

Flutter, SQLite, Spring Boot, MySQL, RabbitMQ, Keycloak, Angular, ESC/POS printers, GitLab CI/CD · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: Software Engineering (mobile + back-end). Interest in distributed systems and offline-first design.

REF-DEV05

Solar client and installer platform: maintenance, monitoring and catalogue for a Tunisian PV distributor

EngineeringMaster

A Tunisian distributor of photovoltaic equipment (panels, inverters, batteries, solar pump drives) works with a network of more than 60 approved installers across the country. Build its digital platform: a mobile app for clients to register their installation, activate the warranty, request maintenance or emergency support and follow their system's real-time production and health; a mobile app for installers to receive and close interventions in the field, even offline; and a web back-office to dispatch requests, manage warranties and see the whole installed base. The platform also detects under-performing systems and turns them into maintenance requests before the client notices.

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Key tasks

  • client app (installation, warranty, maintenance requests, monitoring, catalogue, find an installer, quote request)
  • installer app (interventions, checklists, photos, commissioning report, offline mode)
  • monitoring from inverter data (production, uptime, alarms, expected vs actual yield)
  • smart alerts and SAV dispatch
  • back-office and dashboards
  • French / Arabic

Tools (preliminary proposal)

Flutter, Spring Boot, Keycloak, RabbitMQ, MySQL, TimescaleDB, Angular, inverter and solar-yield APIs · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: Software Engineering (mobile + back-end). Interest in energy, IoT data and field operations.

Data Projects 2

REF-DATA01

DataCollect: fast, reliable survey and statistical data collection

EngineeringMaster

Surveys and field studies are often slow and hard to trust. Build a configurable data collection platform: questionnaires defined by configuration, an offline web app for field agents, real-time quality control (consistency rules, paradata such as interview duration and GPS, outlier and fabrication detection), sampling and weighting, and dashboards that publish results within hours. This product belongs to its founders and is developed at UPTech: an IP and confidentiality agreement is signed at the start.

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Key tasks

  • questionnaire designer (config-driven)
  • offline web collection (PWA) with sync
  • streaming quality checks and fraud detection
  • weighting and margin-of-error computation
  • dashboards

Tools (preliminary proposal)

PWA (React or Angular), Spring Boot or FastAPI, RabbitMQ, ClickHouse/PostgreSQL, dbt, Superset, Python (pandas, statsmodels) · Docker for local tests, Kubernetes for staging and production

Profile

Master or Engineering: Data Engineering / Statistics / Software.

REF-DATA02

Project and IT cost performance analytics (EVM + FinOps)

LicenceMaster

Build UPTech's analytics for project delivery and IT costs. Connect project tools (GitLab/Jira, timesheets) and infrastructure usage (VMware, Kubernetes) into one data model, compute Earned Value indicators (cost and schedule performance), the cost of each service and client, and forecasts. Interactive dashboards help managers see which projects drift and where infrastructure money goes.

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Key tasks

  • data mesh–style data products per domain
  • EVM (PV, EV, AC, CPI, SPI)
  • showback/chargeback of infrastructure per service and client
  • forecasting (cost and delay)
  • dashboards

Tools (preliminary proposal)

PostgreSQL, dbt, Airbyte, Apache Superset, Python, GitLab API, VMware/Kubernetes usage exports · Docker for local tests, Kubernetes for staging and production

Profile

Licence or Master: Business Intelligence / Data Analytics / Information Systems.

Embedded Solution 1

REF-RFID01

Smart RFID stock: reader integration, stock events and demand forecasting

LicenceEngineering

Extend UPTech's RFID inventory solution from scanning to a smart stock system, using UPTech's RFID devices only (UHF readers and tags, no extra boards): a reader connector filters and de-duplicates tag reads, events flow through MQTT to the stock platform, the stock platform keeps a full movement history, and machine-learning models forecast demand and flag anomalies (shrinkage, misplaced items, abnormal consumption).

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Key tasks

  • reader integration (LLRP or reader SDK, read filtering, de-duplication, buffering)
  • MQTT event pipeline
  • stock service and movement history
  • forecasting and anomaly detection
  • dashboard and alerts

Tools (preliminary proposal)

UHF RFID readers and tags (LLRP or reader SDK), MQTT, Spring Boot, Angular, PostgreSQL/TimescaleDB, Python · Docker for local tests, Kubernetes for staging and production

Profile

Licence or Engineering: Embedded Systems / IoT / Software.

DevOps & Platform Projects 1

REF-DEVOPS01

Platform engineering: a secure internal developer platform on Kubernetes

EngineeringMaster

UPTech runs its own CI/CD pipelines and Kubernetes clusters. Turn them into a product for developers: an internal developer platform where a team creates a new service from a template and gets, in minutes, a repository, a secure pipeline, GitOps deployment, secrets and monitoring. Security is built in: signed images, SBOMs, policy-as-code and vulnerability gates.

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Key tasks

  • developer portal (Backstage) and golden-path templates
  • GitOps with Argo CD for staging and production (Kustomize overlays, ApplicationSet, approval in production)
  • supply-chain security (SBOM, signing, Kyverno policies)
  • secrets with Vault
  • DORA metrics dashboard

Tools (preliminary proposal)

Kubernetes, Argo CD, GitLab CI, Backstage, Kyverno, Trivy, Cosign, Syft, HashiCorp Vault, Kustomize, Helm, Prometheus, Grafana · Docker for local tests, Kubernetes for staging and production

Profile

Engineering or Master: DevOps / Cloud / Security.

IT & Security Projects 1

REF-IT01

Security operations centre (SOC) integrated with the service desk

LicenceEngineeringMaster

Build and run UPTech's security operations centre (SOC) on open-source tools, fully integrated with the service desk. A SIEM (Wazuh) collects and correlates logs from servers, network and applications; intrusion detection watches the network; detection rules are mapped to MITRE ATT&CK and proven with simulated attacks. Every security alert becomes an ITSM incident with the right priority, an investigation and response playbook and an escalation path, and the SOC is measured like a real one: time to detect, time to respond, false positives. Vulnerabilities are detected and hosts hardened, backups and recovery are tested, and agents, rules and playbooks are deployed as code (Ansible) and documented.

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Key tasks

  • SIEM (Wazuh) and network IDS (Suricata)
  • detection engineering mapped to MITRE ATT&CK, tested with simulated attacks
  • alert → ITSM incident with SLA, response playbook and escalation
  • SOC processes: triage, investigation, incident response, on-call
  • vulnerability detection and CIS hardening
  • backup and disaster-recovery tests
  • SOC dashboards (MTTD, MTTR, false positives)
  • Ansible automation

Tools (preliminary proposal)

Wazuh, Suricata, Sophos XG, ITSM, Prometheus, Grafana, Ansible, Atomic Red Team, VMware · Docker for local tests, Kubernetes for staging and production

Profile

Licence, Master or Engineering: Cybersecurity / Networks / Systems, with a strong interest in blue-team operations and incident response.

Design Projects: Marketing, Graphic Design and UI/UX 4

These four projects are for marketing, graphic design and UI/UX profiles, not for developers. Their main goal is always marketing, graphic design or UI/UX work: research, concepts, identity, interfaces, campaigns and their results. A project can be extended with a small development mission (for example the design tokens or a landing page in code) when it is agreed in the project charter, but this is never its main goal.

REF-DESIGN01

UPTech design system and UX for the operations platform

LicenceMaster

UPTech's internal tools look and behave differently, and ThePortal modules (call center, performance) already have mock-ups made in different files. Create one design system — colours, typography, components, icons, accessibility rules — and use it to redesign the key screens of an AI-assisted service desk: how an AI suggestion, its evidence and its confidence are shown, and how a technician approves or rejects it. Test the designs with real technicians.

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Key tasks

  • UX research with support technicians
  • design tokens and component library in Penpot, handed over to the developers
  • "explainable AI" patterns: evidence, confidence, approve/reject
  • accessibility (WCAG 2.2 AA)
  • usability tests

Tools (preliminary proposal)

Penpot, Style Dictionary (design tokens), usability testing tools · optional development extension: Storybook

Profile

Licence or Master: UI/UX Design / Digital Media / Graphic Design. A UI/UX project, not a development project: the developers build the screens from your designs. Basic HTML/CSS is a plus and allows an optional extension (the components in Storybook).

REF-DESIGN02

Launch campaign for a new product

LicenceMaster

A new product is about to reach the market, and it needs a launch campaign that people remember and act on. You will design and run it end to end: audience and message research, the campaign concept (big idea, key messages, visual line), and the content: key visual, landing-page design, social media series, a product demo video with short cut-downs, an e-mail sequence and a sales kit. You then publish the campaign in phases (teaser, launch, follow-up), measure it with self-hosted analytics and improve it while it runs. The product name is shared after the interview, under a confidentiality agreement.

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Key tasks

  • audience and competitor research
  • campaign strategy and concept
  • key visual and content system
  • demo video and motion cut-downs
  • landing page and e-mail designs
  • phased publishing
  • A/B tests and campaign analytics

Tools (preliminary proposal)

Penpot (self-hosted), Inkscape or Illustrator, GIMP or Photoshop, Kdenlive or After Effects, Matomo, Listmonk or Mautic, Formbricks

Profile

Licence or Master: Digital Marketing / Graphic Design / Visual Communication. A marketing and design project, not a development project: the web team builds the landing page from your designs. A portfolio is required; motion skills are a plus.

REF-DESIGN03Confidential client

Complete brand identity for a new digital product (confidential)

LicenceMaster

A new digital product is about to launch, and it needs a brand from scratch. You will build its complete identity: brand strategy (positioning, values, personality), visual identity (logo system, colours, typography, iconography, imagery), verbal identity (tagline, tone of voice), a UI kit as design tokens for the product teams, and the launch kit (website and app visuals, social media, presentation and document templates, a short motion video). You test the identity with real target users and see it go live. The product name is shared only after the interview, under a confidentiality agreement.

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Key tasks

  • discovery workshops and competitor audit
  • brand platform
  • logo system and visual identity
  • tone of voice
  • UI kit and design tokens
  • brand guidelines
  • launch kit
  • user testing of concepts

Tools (preliminary proposal)

Penpot (self-hosted), Inkscape or Illustrator, GIMP or Photoshop, Scribus, Kdenlive or After Effects, design tokens, self-hosted survey tools

Profile

Licence or Master: Graphic Design / Visual Communication / Branding / Marketing. A design project, not a development project: the product team applies your design tokens in the code. A portfolio is required.

REF-DESIGN04Confidential client

Re-branding of an existing product for an external client (confidential)

LicenceMaster

An existing product of one of our clients has outgrown its brand. You will lead its re-branding from start to finish. First you audit the current brand and what users think of it, and decide what to keep (brand equity) and what to change. Then you design the new identity and plan the migration of every touchpoint: website, application UI, documents, social media and signage. Finally you roll it out with the client's teams and measure perception before and after. The client and product are shared after the interview, under a confidentiality agreement.

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Key tasks

  • brand audit and asset inventory
  • user and stakeholder perception study
  • evolution-or-revolution strategy
  • new visual and verbal identity
  • design tokens for the product UI
  • migration plan and rollout
  • before/after measurement

Tools (preliminary proposal)

Penpot (self-hosted), Inkscape or Illustrator, GIMP or Photoshop, Scribus, Kdenlive or After Effects, design tokens, self-hosted survey tools

Profile

Licence or Master: Graphic Design / Visual Communication / Branding / Marketing; interest in client work and project management. A design project, not a development project: the client's teams apply the new identity in their code. A portfolio is required.

How to apply
  • Apply only through our web form, before 25 November 2026. Choose your track and your first-choice project (REF code). You can apply alone or as a pair (say so in the form); we confirm whether the project can take two students.
  • We contact you by e-mail with all the details.
  • You receive a short mission based on the project you selected. Reply to it by e-mail within 7 days.
  • Shortlisted candidates are invited to an interview, where you present your mission.

Using AI for your mission is fine: say how you used it, and be ready to explain every part of your work. We are not trying to trap you; we want to find the project where you will do your best work.

Apply now

Applications are closed. Thank you to everyone who applied; follow UPTech on LinkedIn for the next calls.

Good to know

  • Application deadline: 25 November 2026.
  • You may be offered another project that fits your profile better than your first choice.
  • Applications are accepted only through the web form. After that, all communication is by e-mail; applications sent by e-mail or LinkedIn are not considered.
  • Your reply to the project mission decides whether you are shortlisted for an interview.
  • Internships are full time, hybrid (office visits based on project needs), start in February 2027 and last 3 to 6 months depending on your school.
  • The internship is unpaid. A motivation bonus (training, certification or subscription) may be offered at the end, based on results.
  • The tools listed for each project are a preliminary proposal and may change after the study phase.
  • Some projects are for real clients or founder-owned products: a confidentiality (NDA) or IP agreement is signed before the start, and names are shared after the interview.