AI Developer with Python for Customer Care AI Platform team (hybrid)

-> A quick note before you dive in: we're hiring a junior-to-mid developer who wants to grow with a mature team. What follows describes an ambitious product and the technologies you'll get to learn here.

About the team:

Our mission is to build a modern ecosystem used for all IONOS customer support needs. The tools developed by us are used in over 20 locations, by more than 2.000 users, supporting 8 million customer contracts in 10 markets.

The development team has full responsibility for the development lifecycle. This means we plan, develop, test and deploy our software without any other internal or external dependencies.

Our portfolio revolves around an internally built CRM which is now being enhanced with AI capabilities.

About the product you will be building:

We are building a next-generation AI platform designed to redefine how our company interacts with customers. This isn't just a chatbot; it's a high-performance, multimodal AI ecosystem powered by state-of-the-art Speech-to-Speech (S2S) models, advanced Large Language Models (LLMs), and intelligent orchestration frameworks. Our platform will understand, reason, and respond across text and voice — while seamlessly executing real-time actions to resolve customer needs.

We are aiming for a hybrid architecture of Open Source LLMs, industry-leading proprietary models, and Model Context Protocol (MCP) to enable contextual reasoning, tool invocation, and seamless orchestration across systems. The goal is not just to talk to the customer, but to act on their needs.

What makes this project unique:

• The Voice Frontier: We are building low-latency, emotive speech-to-speech pipelines for a truly natural voice channel experience.
• Deep System Integration: Our platform connects directly to the company's core systems via MCPs, allowing the AI to access real-time customer context and execute complex workflows.
• Self-Evolving Logic: We are developing an automated QA and evaluation module that continuously analyzes interactions across channels. By programmatically measuring quality, accuracy, latency, and resolution outcomes, we can close the feedback loop, and adapt system behavior in hours, not weeks.
• Hybrid Innovation: You’ll work at the intersection of "build vs. buy," integrating the best of the open-source community with custom-built internal infrastructure.

What's in it for you:

You won't just be shipping code; you’ll be part of making this concept evolve and

shift. You’ll join a friendly, experienced team where your voice matters and your contribution shapes real-world outcomes. You’ll work in a modern environment with technologies and practices that help us ship reliable software efficiently.

Role description:

As an AI Engineer on this team, you will build the core intelligence systems behind our multimodal AI platform. You will be responsible for moving beyond simple chat interfaces to build high-performance, real-time systems that handle complex reasoning, deep context retrieval, LLM orchestration, retrieval-augmented generation (RAG) and seamless voice interactions.

Main responsibilities:

• Design Agentic Workflows: Design and implement LLM-based systems that go behind response generation - enabling structured tool usage, workflow orchestration, and secure interaction with internal services via MCP (Model Context Protocol).
• Build and Optimize RAG & CAG: Develop high-performance Retrieval-Augmented Generation and Context-Augmented Generation pipelines to ensure accurate, relevant, and low-latency responses. Continuously improve context management, ranking strategies, and grounding mechanisms to support complex, multi-step interactions.
• Voice Channel Mastery: Develop and optimize real-time Speech-to-Speech (S2S) pipelines, focusing on streaming architectures, latency reduction (including Time to First Word - TTFW) and maintaining a natural conversational flow.
• Evaluation, Quality & Alignment: Build and maintain an automated QA module, including LLM-as-a-judge patterns, to measure accuracy, safety, latency, and resolution quality at scale. Translate evaluation insights into systematic models and prompt improvements.
• Model Strategy & Hybrid Integration: Integrate and operate both commercial foundation models (e.g., OpenAI, Anthropic, Google) and open-source alternatives (e.g., Qwen, Kimi, DeepSeek, Moonshot, GLM), selecting and optimizing models based on performance, latency, cost, and use-case requirements.

We are looking for some of:

• Build with LLMs: you've used OpenAI, Anthropic, or similar APIs to build something real, even a personal project (not just tutorials).
• Python comfort: you can write clean scripts, understand functions, read and modify existing code without hand-holding.
• Curiosity about AI: you follow the field, can talk about something you read or tried recently. Self-driven learning matters more than formal credentials.
• A shipped project: a GitHub repo, a small app, a Kaggle notebook. Anything that shows you see things through to completion.
• The vocabulary: you can explain what RAG, embeddings, or a vector database are, even if you haven't built one in production.

What we offer:

• Access to local/international trainings, development and growth opportunities, including access to e-learning platforms, covering both technical and soft skills areas;
• Modern technologies, product responsibility;
• Flexible work schedule;
• Hybrid work option;
• Medical services package from one of two private providers;
• 25 vacation days per year;
• Substitute days off for public holidays that occur on the weekend;
• Meal tickets;
• Internal referral program;
• Team events, networking events organized to promote a passionate, creative and d
• iverse culture;
• Summerfest and Winterfest parties;
• Of course, coffee, soft drinks and fresh fruits are on us in the office.

We are looking forward to meeting you!