Dein persönlicher KI-Karriere-Agent
Integrating LLM APIs into production applications, managing token limits and latency. 2+ years software engineering experience with hands-on AI tools and LLM API integration required. Structured AI certification pathways, vendor fellowship access.
Deine Match-Analyse
Warum dieser Job zu dir passt
Mögliche Lücken
Tipps für deine Bewerbung
Anforderungen
- Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or related field
- 2+ years commercial software engineering experience in production environments or equivalent
- Proficiency in Python, Java, or TypeScript
- Hands-on experience using AI tools in daily engineering work with practical examples
- Direct experience calling LLM APIs in production code with understanding of token management, latency, and cost tradeoffs
- Basic understanding of web technologies (JavaScript, HTML, CSS)
- Familiarity with cloud fundamentals (AWS, Azure, or GCP)
- Familiarity with containers (Docker)
- Familiarity with CI/CD pipelines
- Understanding of Agile delivery fundamentals
- Experience with SQL databases
- Experience with NoSQL databases
- Ability to validate, evaluate, and improve AI-generated outputs
- Understanding of AI limitations and responsible use
- Conceptual understanding of agentic system concepts
- Awareness of orchestration frameworks (LangChain, LangGraph, or equivalent)
- Awareness of RAG pipelines
- Awareness of how full-stack applications connect to agent-based architecture
- Production experience with agentic systems preferred
Aufgaben
- Use AI coding assistants daily to improve productivity and quality
- Integrate LLM APIs into production applications
- Manage token limits and latency for AI provider APIs
- Build initial abstraction layers for LLM integrations
- Apply AI across the software delivery lifecycle
- Generate tests using AI
- Debug code with AI assistance
- Accelerate code review with AI
- Perform prompt engineering for development tasks
- Own the quality of AI-generated outputs
- Exercise engineering judgment on AI reliability and limitations
- Determine when AI output is production-ready
- Define and track KPIs for AI-assisted workflows
- Present AI productivity and quality metrics to stakeholders
- Own end-to-end delivery in Agile sprint cycles
- Provide production support alongside client engineering teams
- Contribute to shared knowledge bases
- Develop reusable components
- Establish internal AI tooling standards
- Build and integrate application layers, APIs, and interfaces
- Connect full-stack systems to agentic backends
- Understand data flows between code and AI pipelines
- Manage context handoffs between code and AI pipelines
- Identify integration points between code and AI pipelines
Berufserfahrung
Ausbildung
Sprachen
Tools & Technologien
Benefits
- Flexible working hours
- Modern work environment
- Vendor fellowship access
- Structured AI certification pathways
- Clear development track
Von Nejo automatisch aufbereitet
Nejo hat diesen Job automatisch von der Website des Unternehmens Accenture erfasst und die Informationen auf Nejo mit Hilfe von KI für dich aufbereitet. Trotz sorgfältiger Analyse können einzelne Informationen unvollständig oder ungenau sein. Bitte prüfe immer alle Angaben in der Originalanzeige! Inhalte und Urheberrechte der Originalanzeige liegen beim ausschreibenden Unternehmen.
Zur Originalanzeige bei AccentureÜber das Unternehmen
Das Unternehmen ist eines der weltweit größten Technologie- und Beratungsunternehmen.
accenture.comNejo bewertet ihn, und bringt ihn danach gemeinsam mit dir in Bestform.
Noch nicht perfekt?
- BCG PlatinionAI Associate(m/w/x)Vollzeitnur vor OrtKeine AngabeWienab 60.000 / Jahr
- AI:AT – the AI Factory AustriaAI Innovation & Solution Engineer(m/w/x)Vollzeitnur vor OrtBerufserfahrenWienab 68.152 / Jahr
- BCG PlatinionAI Architect(m/w/x)Vollzeitnur vor OrtBerufserfahrenWienab 75.000 / Jahr
- EUROPEAN DYNAMICSAI/ML Engineer (Python)(m/w/x)Vollzeitnur vor OrtSeniorWien
- HYPO OOEAppian Developer(m/w/x)Vollzeitnur vor OrtBerufserfahrenWien