Lead AI Engineer (LLMs & Data Pipelines) at Tietoevry1

We are seeking a Lead AI Engineer (LLMs & Data Pipelines) to drive the development and deployment of LLM-powered capabilities across our platforms. In this role, you will build intelligent AI features such as classification, extraction, summarization, and semantic search solutions, while designing scalable RAG and data pipelines for production environments. You will collaborate with engineering, data, and MLOps teams to optimize model performance, reliability, and deployment across diverse hardware environments. Responsibilities • Design and enhance LLM-powered solutions, including classification, extraction, summarization, and action orchestration capabilities • Build and optimize embedding and retrieval pipelines, including RAG architectures and semantic search systems • Develop and maintain robust data pipelines for model training, evaluation, and continuous improvement • Benchmark and validate AI model performance across CPU, GPU, NPU, and DSP environments • Optimize inference runtimes to achieve efficient latency, reliability, and cost in production systems • Define and execute evaluation methodologies, quality gates, and regression detection processes for LLM behavior • Collaborate closely with engineering, data, and MLOps teams to ensure stable and scalable production deployments • Support experimentation and evaluation of prompts, architectures, and model selection strategiesQualifications • Proven experience working with LLMs and NLP systems • Hands-on experience with embeddings, vector databases, and RAG architectures • Strong programming skills in Python and experience with machine learning frameworks • Experience building and maintaining production-grade data pipelines • Solid understanding of evaluation methodologies, regression detection, and model quality assessment • Experience integrating AI models into APIs and real-world production systems • Hands-on experience with inference runtimes such as ONNX Runtime and TensorFlow Lite / LiteRT • Understanding of deployment challenges and optimization strategies across constrained or embedded environments • Experience analyzing model calibration and accuracy/latency trade-offsNice to Have • Experience deploying AI models on edge or embedded devices • Knowledge of scalable AI deployment, monitoring, and MLOps practices • Experience benchmarking models across heterogeneous hardware backends • Familiarity with conversational AI systems and intelligent workflow orchestration • Strong analytical and problem-solving skills • Proactive mindset and ability to work collaboratively in cross-functional teams