Thesis: AI-Driven Observability for Dev Tools

Ericsson

Lunds kommun

On-site

SEK 223,000 - 290,000

Full time

6 days ago
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Job summary

Ericsson in Lund invites a student to work on an analytics and observability framework for an AI-assisted command-line development tool. The project will instrument the tool, collect telemetry, and provide actionable insights into model choices, token use, latency, and costs.

You will design measurements, analyze usage patterns, and validate recommendations through controlled studies, contributing to an open, privacy-respecting data platform that helps developers understand and optimize their AI

Qualifications

  • Knowledge in Computer Science, Software Engineering, Data Science, or related field.
  • Experience with at least one programming language, preferably Python, JavaScript, or TypeScript.
  • Interest in observability, analytics, distributed systems, or AI developer tools.
  • Familiarity with data collection, visualization, experimentation, or statistical analysis is beneficial.
  • Ability to work independently and communicate findings clearly.

Responsibilities

  • Identify signals and metrics for understanding AI-assisted development workflows.
  • Design and implement instrumentation to capture relevant events with privacy in mind.
  • Analyze data to identify cost and latency drivers and propose optimizations.
  • Validate recommendations through controlled cohorts or user studies.
  • Communicate findings and collaborate with engineers and researchers.

Skills

Observability concepts
Python
JavaScript/TypeScript
Data analysis
Independent work

Education

Computer Science/Software Engineering/Data Science

Tools

Telemetry tooling
Dashboard development

Job description

Ericsson in Lund invites a student to work on an analytics and observability framework for an AI-assisted command-line development tool. The project will instrument the tool, collect telemetry, and provide actionable insights into model choices, token use, latency, and costs.

You will design measurements, analyze usage patterns, and validate recommendations through controlled studies, contributing to an open, privacy-respecting data platform that helps developers understand and optimize their AI

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