Senior AI Engineer

Role overview

Responsibilities

• check_circle Design and develop advanced conversational AI solutions leveraging Large Language Models (LLMs) and RAG architectures.
• check_circle Optimize information retrieval pipelines using embeddings, vector search technologies, and reranking models to improve response relevance and accuracy.
• check_circle Design and implement autonomous AI agents capable of utilizing external tools and services through Model Context Protocol (MCP) environments.
• check_circle Build robust data ingestion and semantic indexing pipelines for complex document types, including structured technical documentation, tables, and multi-column PDFs.
• check_circle Implement intelligent chunking and document processing strategies to maximize retrieval effectiveness.
• check_circle Containerize and deploy AI services using modern cloud-native technologies, ensuring scalability, reliability, and maintainability.
• check_circle Monitor model performance, costs, latency, and output quality through LLMOps practices and observability platforms.
• check_circle Analyze prompts, traces, and user feedback to continuously enhance AI system effectiveness and user experience.
• check_circle Collaborate closely with technical teams and business stakeholders to deliver innovative AI-driven solutions.

Basic qualifications

• Proven experience with Python and modern web development frameworks such as FastAPI or Flask for building RESTful APIs.
• Strong hands-on experience with LLM orchestration frameworks, including LangChain, LlamaIndex, or direct LLM API integrations.
• Deep understanding of Retrieval-Augmented Generation (RAG) architectures and semantic search systems.
• Experience working with Vector Databases such as Qdrant, Milvus, Pinecone, Weaviate, or pgvector.
• Solid knowledge of Prompt Engineering techniques and AI agent development through function calling and tool integration.
• Experience with containerization using Docker and orchestration with Kubernetes.
• Familiarity with enterprise cloud environments, particularly Google Cloud Platform (GCP) and/or Microsoft Azure.
• Experience managing source control, CI/CD pipelines, and software delivery processes using GitLab.
• Hands-on experience with LLM observability and monitoring platforms, particularly Langfuse, including prompt tracking, latency monitoring, token usage analysis, and user feedback collection.

Preferred qualifications

• Experience with automated RAG evaluation frameworks such as Ragas or TruLens.
• Strong knowledge of advanced document parsing techniques, including OCR and layout detection.
• Practical experience fine-tuning open-source models using approaches such as LoRA and QLoRA.
• Strong problem-solving mindset and ability to navigate uncertainty within a rapidly evolving AI landscape.
• Product-oriented attitude with a focus on delivering AI solutions that solve real user problems.
• Excellent communication skills and the ability to effectively interact with both technical teams and business stakeholders.