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In office (Pune)

Confirmed on the employer's own hiring board on Nov 3, 2026. First seen by Alion on Nov 3, 2026. Artefact scores B on the Alion truth index.

Overview
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Impact
Profile match
Artefact is a French data and artificial intelligence consultancy founded in 2015 that works on marketing, commercial and operational problems for large consumer-facing companies. Its distinguishing feature is combining data science with marketing execution: it builds the data platforms and models and then runs the campaigns and commerce operations they inform, rather than handing recommendations to an agency. Headquartered in Paris with offices across Europe, Asia, the Middle East and North America, it was taken private by Ardian in 2023 and employs several thousand consultants and data specialists.
Backed by Ardian, Cinven

Artefact is a new generation of data service providers specialising in data consulting and data-driven digital marketing. It is dedicated to transforming data into business impact across the entire value chain of organisations. We are proud to say we’re enjoying skyrocketing growth.

The backbone of our consulting missions, today our Data consulting team has more than 400 consultants covering all Artefact's offers (and more): data marketing, data governance, strategy consulting, product owner…

What We Are Looking For:

  • Hands-on experience building personal, academic, or internship projects using Python or JavaScript/TypeScript.

  • A genuine interest in Artificial Intelligence, especially modern LLMs (OpenAI, Claude, open-source models).

  • Prior hands-on experience, even in small personal sandbox projects, with frameworks like LangGraph or LangChain, alongside core concepts like agents, tool calling, and RAG.

  • Ability to consume REST APIs and build simple end-to-end backend and/or frontend workflows.

  • Technical reading comprehension in English (most AI documentation, research, and libraries are in English).

  • Basic familiarity with vector databases or search backends (e.g., Pinecone, Supabase / pgvector, Chroma, Weaviate).

  • Comfortable using Git for version control, branching, and pull requests.

Nice-to-Haves (Valuable Bonuses):

  • Frontend & App Frameworks: Exposure to Next.js, React, or modern UI libraries (e.g., Tailwind CSS, shadcn/ui) to quickly turn AI flows into functional interfaces.

  • Structured Data & Schemas: Experience enforcing structured outputs from LLMs using schemas (e.g., Pydantic in Python, Zod in TypeScript).

  • LLM Observability & Debugging: Familiarity with tracing or evaluation tools like LangSmith, Langfuse, or Helicone.

  • Deployment Basics: Experience deploying full-stack apps to platforms like Vercel, Railway, or Render, AWS, Azure, GCP.

  • Active Experimentation: A visible GitHub repo, live demo, or hobby project that shows curiosity about the latest AI releases and tooling.
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