Junior AI Native Engineer

Accenture France; Saint-Herblain; Sur place; EUR 75 000 - 100 000; Plein temps

Résumé du poste

Accenture France is seeking an AI Engineer (Software) to design, build, and ship production-grade software across the full stack, using AI-assisted tooling as a standard practice alongside core engineering skills.

You will work on real client programs across industries, integrating AI into production systems and contributing to agentic AI pipelines. This role offers a clear path toward advanced AI-focused engineering tracks.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or a related field.
• Commercial software engineering experience in production environments or equivalent through projects.
• Proficiency in at least one backend language: Python, Java, or TypeScript.
• Hands-on experience using AI tools in day-to-day engineering work, including calling LLM APIs in production.
• Basic understanding of web technologies (JavaScript, HTML, CSS).
• Familiarity with cloud fundamentals (AWS, Azure, or GCP), containers (Docker), and CI/CD pipelines.
• Understanding of Agile delivery fundamentals.
• Experience with databases — SQL or NoSQL.
• Ability to evaluate AI outputs and understand limitations for production use.

Responsabilités

• Use AI coding assistants daily to improve delivery productivity and quality.
• Integrate LLM APIs into production applications and manage token limits and latency.
• Apply AI across the full software delivery lifecycle including AI-generated tests and debugging.
• Ensure quality and reliability of AI-generated outputs within delivery scope.
• Define KPIs to evaluate AI-assisted workflows and present metrics to stakeholders.
• Own end-to-end delivery in Agile sprint cycles with client teams.
• Contribute to shared knowledge bases and internal AI tooling standards.
• Build application layers, APIs, and interfaces connecting full-stack systems to AI backends.

Connaissances

Backend development

Python

Java

TypeScript

AI tooling

LLM APIs

Web fundamentals

Cloud fundamentals

CI/CD

Agile

Formation

Bachelor's degree in CS/Engineering

Outils

Docker

SQL

NoSQL

Description du poste

Job Description

We are building the next generation of AI-native engineering talent engineers who use AI as a core part of how they work, not as an add-on. As an AI Engineer (Software), you will design, build, and ship production-grade software across the full stack, using AI-assisted tooling as standard daily practice alongside your core engineering skills.

Job Description

We are building the next generation of AI-native engineering talent engineers who use AI as a core part of how they work, not as an add-on. As an AI Engineer (Software), you will design, build, and ship production-grade software across the full stack, using AI-assisted tooling as standard daily practice alongside your core engineering skills.

You will work on real client programs across industries, building production-grade software that connects to and supports agentic AI systems — understanding how your full-stack work integrates with agent architecture, LLM APIs, and enterprise AI pipelines. This is not a stepping-stone role: it is a core engineering function in the most in-demand part of the market, with a direct pathway to the Forward Deployed Engineer program for those who develop agentic depth.

We offer what no single product company can: breadth across every industry, every enterprise technology stack, and every level of organizational complexity — combined with vendor fellowship access inside Anthropic, OpenAI, Microsoft, and Google engineering teams, structured AI certification pathways, and a clear development track toward agentic and forward-deployed engineering.

Key Responsibilities

• Use AI coding assistants daily as a standard part of delivery, actively, frequently, and with demonstrable impact on productivity and output quality
• Integrate LLM APIs into applications in production: calling AI provider APIs in live code, managing token limits and latency, and building initial abstraction layers
• Apply AI across the full software delivery lifecycle: AI-generated tests, AI-assisted debugging, AI-accelerated code review, and prompt engineering for development tasks
• Own the quality of AI-generated outputs in your delivery scope, exercise engineering judgment about reliability, limitations, and failure modes; know when AI output is production-ready and when it is not
• Define and track KPIs to evaluate the effectiveness and ROI of AI-assisted workflows; present AI productivity and quality metrics to project stakeholders
• Own delivery end-to-end — from design through to production support — in Agile sprint cycles alongside client engineering teams
• Contribute to shared knowledge bases, reusable components, and internal AI tooling standards that benefit the wider team
• Build and integrate the application layers, APIs, and interfaces that connect full-stack systems to agentic backends — understanding data flows, context handoffs, and integration points between your code and AI pipelines

Job Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field
• Commercial software engineering experience in production environments (or equivalent demonstrated through academic projects, internships, or shipped personal projects)
• Proficiency in at least one primary backend language: Python, Java, or TypeScript
• Demonstrated hands-on experience using AI tools actively in day-to-day engineering work — with practical examples of how AI was used to solve real problems, iterate on outputs, and improve delivery; including direct experience calling LLM APIs in production code with an understanding of token management, latency, and cost tradeoffs
• Basic understanding of web technologies including JavaScript, HTML, and CSS
• Familiarity with cloud fundamentals (AWS, Azure, or GCP), containers (Docker), and CI/CD pipelines
• Understanding of Agile delivery fundamentals
• Experience with databases — SQL or NoSQL
• Ability to validate, evaluate, and improve AI-generated outputs; understanding of AI limitations and responsible use
• Familiarity with agentic system concepts — awareness of orchestration frameworks (LangChain, LangGraph, or equivalent), RAG pipelines, and how full-stack applications connect to agent-based architecture; production experience preferred, conceptual understanding required