About the job

My client is building enterprise-scale AI products used by millions of end users. They're looking for experienced AI Engineers who enjoy solving difficult engineering problems rather than simply integrating APIs. This is a role where you'll build production-grade AI systems that need to be reliable, scalable and cost efficient.

You'll work on everything from agentic AI platforms and enterprise search through to Voice AI, Document AI and intelligent workflow automation, collaborating with experienced engineers in a highly technical environment.

The OpportunityYou'll design and build AI-powered software that operates at enterprise scale, with deployments supporting tens of millions of users in on-premise environments.

• Projects include:Building AI-native products where LLMs are embedded directly into user workflows
• Developing autonomous AI agents using modern agentic frameworks
• Creating intelligent enterprise search across structured and unstructured data
• Designing Document AI pipelines for extraction and classification
• Developing Voice AI applications using real-time streaming architectures
• Automating complex business processes using Generative AI
• Taking research papers and turning them into robust production systems

• This is a hands-on engineering role where software quality is valued just as highly as AI capability.What You'll Be DoingDesign and develop production-ready AI applications
• Build scalable LLM and RAG pipelines
• Develop agentic systems using modern orchestration frameworks
• Optimise AI workloads for performance and infrastructure efficiency
• Build APIs and backend services in Python
• Contribute to frontend components where required using TypeScript
• Work with Kubernetes-based infrastructure and CI/CD pipelines
• Collaborate closely with product teams and customers to solve real business problems
• Participate in architecture discussions, code reviews and technical knowledge sharing

• Tech StackDepending on the project, you'll work with technologies including:LanguagesPython
• TypeScript
• Rust (nice to have)
• AI & Machine LearningLLMs
• Transformers
• Embeddings
• PyTorch
• Hugging Face
• scikit-learn
• LiteLLM
• Agentic AILangChain
• LangGraph
• MCP
• Tool Calling
• Durable Execution
• BackendFastAPI
• Pydantic
• AsyncIO
• InfrastructureDocker
• Kubernetes
• Redis
• PostgreSQL
• Elasticsearch
• ObservabilityOpenTelemetry
• Prometheus
• Grafana

• What They're Looking ForYou'll likely have experience with several of the following:Strong Python software engineering skills
• Experience building production AI or machine learning systems
• Solid understanding of LLMs, embeddings and transformer architectures
• Experience developing scalable backend services
• Knowledge of modern software engineering practices including testing, Git workflows and CI/CD
• Experience with containerisation and Kubernetes
• Strong problem-solving ability and an engineering-first mindset
• Fluent German communication skills

• Nice to HaveExperience in one or more of the following would be particularly valuable:Fine-tuning LLMs
• Model optimisation or inference optimisation
• Enterprise AI deployments
• AI agents used in production
• Voice AI
• Document AI
• Enterprise search
• Software security or regulated environments
• Jenkins and advanced DevOps tooling
• Rust

• Why Join?Work on AI systems deployed at enterprise scale
• Build products used by millions of users
• Work across multiple AI domains including LLMs, Voice AI and intelligent automation
• Highly collaborative engineering culture with regular technical knowledge sharing
• Modern technology stack with a strong focus on engineering quality
• Opportunity to work on genuinely challenging AI problems rather than proof-of-concepts
• Remote-first working with long-term career growth

·        Generative AI and LLM Engineering: LLM Application Development, Retrieval Augmented Generation (RAG), Prompt Engineering, LangChain, LangGraph, AI Agents, Multi Agent Systems, Model Context Protocol (MCP), Hugging Face Transformers, OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Groq

·        Vector Search and Data Engineering: ChromaDB, FAISS, Pinecone, Sentence Transformers, Semantic Search, Python, SQL, PostgreSQL, Pydantic

·        Machine Learning and Deployment: Deep Learning, TensorFlow, PyTorch, Natural Language Processing, Computer Vision, FastAPI, SQLAlchemy, Alembic, Docker, Docker Compose, MLOps, Microsoft Azure, CI/CD

·        Analytics, Tools and Methods: Tableau, Power BI (DAX), Git, GitHub, JWT Authentication, REST API Design, Agile Delivery