Junior Applied AI Engineer (all genders)

Accenture DACH; Kronberg im Taunus; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Zusammenfassung

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

You will own end-to-end delivery in Agile sprints, integrate LLM APIs into live applications, and contribute to reusable AI components while ensuring responsible use of AI in production.

Qualifikationen

• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or related field
• Proficient in at least one backend language (Python/Java/TypeScript)
• Hands-on experience using AI tools in day-to-day engineering work
• Experience calling LLM APIs in production with token management and latency awareness
• Familiarity with web technologies (JavaScript, HTML, CSS)
• Cloud fundamentals (AWS/Azure/GCP), containers (Docker), and CI/CD pipelines
• Understanding of Agile delivery fundamentals
• Experience with databases (SQL/NoSQL)
• Awareness of agentic systems concepts and orchestration frameworks

Aufgaben

• Use AI coding assistants daily to improve productivity and output quality
• Integrate LLM APIs into production applications and manage token limits and latency
• Apply AI across the full software delivery lifecycle with AI-generated tests and AI-assisted debugging
• Own the quality of AI-generated outputs and assess production readiness
• Define and track KPIs for AI-assisted workflows and report to stakeholders
• Own delivery end-to-end in Agile sprint cycles with client engineering teams
• Contribute to reusable components and internal AI tooling standards
• Build and connect application layers and APIs to agentic backends across full-stack systems

Kenntnisse

Python

Java

TypeScript

LLM APIs

AI tooling

Web basics

Cloud platforms

Docker

CI/CD

SQL/NoSQL

Agile

LangChain/LangGraph

Ausbildung

Bachelor's degree in Computer Science/Engineering

Tools

Docker

LangChain

CI/CD tooling

Database tooling

Jobbeschreibung

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. 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
• Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or a related field
• Exposure in 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