ING

Financial Services

AIEngineerIntern

€20–30k ~AI est. Milan, Italy FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Entry candidates.

The Brief

“AI Engineer Intern at ING. Skills: AI system development, Agentic architecture, Prompt engineering, RAG systems. Design AI systems. Develop AI systems”

Industry & Context.

Financial Services
Problems you'll solve

Problem-solving abilities

What They're Looking For.

Must Have

Proficiency in Python, Proficiency in TypeScript, Implement ethical guardrails, Identify and mitigate biases, Protect data throughout AI lifecycle, Ensure system integrity

Nice to Have

MLOps and deployment experience

What You'll Do.

Build multi-agent systems

Develop effective prompts

Implement RAG systems

Develop evaluation frameworks

How You'll Work.

Team & Collaboration

Cross-functional collaboration

Full Job Description

In ING valorizziamo e supportiamo i nostri dipendenti prima di reclutare talenti esterni. Se ritieni che questo posto vacante sia il giusto step successivo nella tua carriera in ING, ti invitiamo a candidarti. Se hai domande sul posto vacante o hai bisogno di saperne di più prima di ritenere di poterti candidare. Non esitare a contattare il recruiter responsabile. An AI Engineer is a technical professional specialized in the design, development, and deployment of artificial intelligence systems in production, leveraging advanced skills in software engineering, large language models, and other cutting-edge technologies. They also play a key role in assessing the ethical impact of their solutions, ensuring that AI systems are developed responsibly, transparently, and in compliance with privacy and security regulations. Key Responsibilities • End-to-end AI system development: Design, develop, and deploy artificial intelligence systems from prototype to production • Agentic architecture implementation: Build multi-agent systems using agent architectural patterns • Prompt engineering and optimization: Develop effective prompts for generative models to ensure consistent performance across different model families • RAG systems: Implement and optimize semantic search and retrieval-augmented generation systems for enterprise applications • Evaluation and testing: Develop robust evaluation frameworks to measure the performance, reliability, and alignment of AI agents Requirements • Programming languages: Proficiency in Python and/or TypeScript for AI application development • Frameworks and tools: Experience with LangChain, LangGraph, Copilot SDK, ADK, or other frameworks for building autonomous agents • Models and architectures: Understanding of transformers, fine-tuning techniques (LoRA, QLoRA, DPO), and multimodal models • Vector databases: Implementation and management of embedding and retrieval systems using databases • Language skills: For specific projects, language e

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