Senior GenAI Engineer with German

One of our clients operates prominently in the financial sector, where we enhance operations across their extensive network of 150,000 workstations and support a workforce of 4,500 employees. Our IT solutions ensure streamlined processes and heightened security, enabling them to maintain leadership in financial technology.

Responsibilities  

Prompt Engineering & Enhancement : Design, secure, extend, and customize system prompts (e.g., chat history, instructions, rules); evaluate the impact of prompt changes and continuously improve LLM usage scenarios while analyzing risks and benefits. 

RAG Systems Development: Design and optimize retrieval pipelines, including document chunking, embedding generation, and ranking strategies. Build and maintain solutions using vector databases (e.g., FAISS, Weaviate, Pinecone) or Postgres with pgvector, to enable accurate and context-aware responses. 

Context Management & Optimization: Implement advanced context window optimization, memory strategies, and token efficiency techniques to ensure scalable and cost-effective LLM interactions. 

LLM Evaluation, Monitoring & Tracking: Evaluate new LLM releases, monitor response quality and consistency, and build automated evaluation pipelines to assess performance across use cases. 

AI Safety & Guardrails Implementation: Design and implement prompt injection protection, output filtering, and policy enforcement mechanisms to ensure safe and compliant AI behavior in enterprise environments. 

System Integration & APIs: Develop and expose AI capabilities via scalable APIs, ensuring proper integration into existing systems and workflows. 

Deployment, Serving & Scalability: Design and manage serving layers for LLM applications, addressing scaling strategies, latency considerations, and reliability in both cloud and on-prem environments 

Observability & Reliability: Implement logging, monitoring, and alerting mechanisms to ensure visibility into system performance, usage patterns, and failures.

Python Development & Prototyping: Develop prototype and production-level Python code to support AI features, ensuring reliability and maintainability aligned with product requirements. 

Knowledge Sharing & Research: Stay up to date with the latest techniques in reasoning, chaining, and context optimization; contribute to AI Labs community discussions and share findings with the Data Science team.