Quality Assurance Intern & Customer Support Intern - PFE 2027

• Atlanta, GA, US
• Tunis, Tunisia
• Istanbul, Turkey
• Paris, France
• Porto, Portugal

Our values

• Be nice and trustworthy
• Put our customers first
• Succeed together
• Never stop improving

Are you ready to grow with us? To find out more about Cognira, please visit our website at www.cognira.com

These two roles are available in both our Tunis and Sfax offices.

Please submit your resume in English.

I- Quality Assurance Internship Topics

Topic 1: AI-powered test management tool, from requirements to risk-based regression

Cognira | QA Team

ABOUT THE INTERNSHIP

Before development: an AI assistant reviews the Jira user stories and flags vague, missing or untestable acceptance criteria and forgotten edge cases, with clarifying questions for the PO.

During testing: writes structured test cases with reusable preconditions, links them to Jira stories, groups them into test executions per release, and records results with evidence and linked bugs. Traceability with the automated Playwright scenarios is kept throughout.

Before each release: the assistant learns from past releases, bugs and test results to predict the riskiest features. It orders the regression scenarios so the riskiest are tested first and suggests new targeted scenarios, which QA reviews before adding them.

→ The result is one shared tool that helps QA catch gaps early, test smarter, and let fewer regressions reach clients

WHAT YOU'LL WORK ON

• Requirements and design document, validated with the QA team
• Test management module: test cases, preconditions, executions, results and history, Jira links
• Migration of existing test cases, plus traceability with automated tests
• AI story review: unclear or missing acceptance criteria, testability score
• AI regression prioritization: risk score per feature and suggested new scenarios
• QA review screen for AI suggestions
• Pilot on a real release, with metrics
• Integration into the Cognira Test Hub

WHAT YOU'LL BRING

• TypeScript and a modern frontend framework
• REST API design
• Data modeling and databases (SQL)
• Python
• Basics of AI / LLMs and machine learning
• Git

NICE TO HAVE

• Jira API
• NLP / embeddings, retrieval (RAG)
• QA / test management concepts
• Playwright
• UX design
• Azure

Topic2: AI-based framework for Hybrid API & UI Performance Testing (k6 / xk6)

Cognira | QA Team

ABOUT THE INTERNSHIP

• A custom k6 binary built with xk6, shipped as a versioned Docker image.
• A TypeScript framework with reusable API, browser and hybrid scenarios (smoke, load, stress, spike, soak).
• An in-house Go extension for k6 (auth tokens shared across virtual users).
• One configuration profile per client environment, with SLOs and load limits.
• GitHub Actions workflows: on demand, nightly and after each deployment.
• Grafana dashboards, an HTML report per run, and a first baseline for each client environment.
• Technical documentation and the internship report.

WHAT YOU'LL WORK ON

• A custom k6 distribution: an xk6 build workflow in GitHub Actions, a versioned Docker image and a changelog.
• A framework and scenario library: a TypeScript core, API and browser journeys, hybrid scenarios and workload profiles.
• An in-house xk6 extension: Go source, unit and integration tests, and usage examples.
• Environment profiles: one profile per client environment, with SLOs and load budgets agreed with their owners.
• CI/CD workflows: on-demand, scheduled and post-deployment runs, with one job per environment.
• Dashboards and reports: Grafana dashboards for a single run, trends and cross-client comparison, and an HTML report per run, published next to the Allure reports.
• A first baseline campaign: baseline results per client environment for the critical journeys.
• Documentation: architecture, “add a scenario” and “add an environment” guides, a safety runbook and the internship report.

WHAT YOU'LL BRING

• JavaScript / TypeScript
• HTTP and REST APIs
• Docker and Git
• CI/CD basics
• Willingness to learn Go

II- Customer Support Internship Topic

Topic 1 : AI-Assisted Log Analysis and Incident Reproduction

Cognira | Customer Success

ABOUT THE INTERNSHIP

The objective of this internship is to develop an AI-assisted tool that helps the Customer Support (CS) team investigate client-reported incidents by analyzing application logs, reconstructing the sequence of events leading to unexpected behavior, and generating evidence-based reproduction steps and investigation reports.

The tool will analyze authorized application logs to identify relevant errors, failed operations, anomalies, and correlated events. By combining log evidence with incident descriptions, it will help reconstruct what happened, identify potential root causes, and generate structured bug report drafts containing relevant evidence, reproduction steps, and missing information.

Beyond individual investigations, the project aims to transform the findings from investigated incidents into a structured and reusable knowledge base. This repository will progressively consolidate historical client issues, affected modules, reported symptoms, log patterns, reproduction steps, confirmed root causes, resolutions, and related tickets where available.

Building on this knowledge base, the project will explore an AI-assisted incident classification mechanism that can categorize new client issues and identify similar previously investigated cases. By comparing new incidents with historical findings, the tool will suggest relevant problem categories, potential root causes, and previously documented investigation paths or resolutions, helping the CS team recognize recurring issues and reduce duplicated troubleshooting efforts.

The overall objective is to establish a continuous workflow in which log analysis supports incident investigation, the results enrich historical incident knowledge, and that accumulated knowledge improves the classification and investigation of future issues.

The solution will operate in read-only mode using authorized data sources and approved historical or test incidents. All generated findings and classifications will remain subject to human review, with a clear distinction between observed evidence, inferred hypotheses, and confirmed root causes.

The internship will last four months, with the goal of delivering a functional proof of concept demonstrating the feasibility and value of this integrated approach on a selected set of incidents.

WHAT YOU'LL WORK ON

• Process authorized application logs to identify errors, anomalies, and related events.
• Incident Reconstruction and Reproduction: Reconstruct incident timelines and generate evidence-based reproduction steps and potential root causes.
• AI-Assisted Investigation and Bug Reporting: Generate structured investigation summaries and bug report drafts with supporting evidence, reproduction steps, and confidence indicators.
• Historical Incident Knowledge Base: Build a structured repository of investigated incidents, including symptoms, log patterns, root causes, reproduction steps, and resolutions.
• Incident Classification and Historical Matching: Classify new incidents into relevant categories and identify similar historical cases, potential root causes, and documented resolutions.
• CS-Friendly Interface: Provide a simple interface or structured output for reviewing investigation results, evidence, and classification suggestions.
• Evaluation and Documentation: Evaluate the tool's accuracy, usefulness, and potential time savings, and document its architecture, limitations, and future improvements.

WHAT YOU'LL BRING

• Basic to intermediate programming skills in Python.
• Understanding of log files, application errors, exceptions, and debugging concepts.
• Knowledge of data processing, text analysis, and pattern recognition.
• Familiarity with APIs, databases, and structured data formats such as JSON.
• Analytical thinking and strong problem-solving skills.
• Ability to document technical findings clearly and translate them into actionable investigation steps.

NICE TO HAVE

• Experience with Large Language Models (LLMs), AI agents, or generative AI APIs.
• Familiarity with Retrieval-Augmented Generation (RAG), embeddings, or semantic search.
• Knowledge of anomaly detection, time-series analysis, or event correlation.
• Experience with observability and logging tools such as Grafana, Kibana, Elasticsearch, or the ELK stack.
• Familiarity with SQL and database querying.
• Understanding of SaaS applications, incident management, or software testing.
• Experience integrating AI tools into existing workflows.