Lead AI Engineer (LLMs & Data Pipelines)

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 capabilitiesBuild and optimize embedding and retrieval pipelines, including RAG architectures and semantic search systemsDevelop and maintain robust data pipelines for model training, evaluation, and continuous improvementBenchmark and validate AI model performance across CPU, GPU, NPU, and DSP environmentsOptimize inference runtimes to achieve efficient latency, reliability, and cost in production systemsDefine and execute evaluation methodologies, quality gates, and regression detection processes for LLM behaviorCollaborate closely with engineering, data, and MLOps teams to ensure stable and scalable production deploymentsSupport experimentation and evaluation of prompts, architectures, and model selection strategiesQualifications
Proven experience working with LLMs and NLP systemsHands-on experience with embeddings, vector databases, and RAG architecturesStrong programming skills in Python and experience with machine learning frameworksExperience building and maintaining production-grade data pipelinesSolid understanding of evaluation methodologies, regression detection, and model quality assessmentExperience integrating AI models into APIs and real-world production systemsHands-on experience with inference runtimes such as ONNX Runtime and TensorFlow Lite / LiteRTUnderstanding of deployment challenges and optimization strategies across constrained or embedded environmentsExperience analyzing model calibration and accuracy/latency trade-offsNice to Have
Experience deploying AI models on edge or embedded devicesKnowledge of scalable AI deployment, monitoring, and MLOps practicesExperience benchmarking models across heterogeneous hardware backendsFamiliarity with conversational AI systems and intelligent workflow orchestrationStrong analytical and problem-solving skillsProactive mindset and ability to work collaboratively in cross-functional teams
At Tieto, we believe in the power of diversity, equity, and inclusion. We encourage applicants of all backgrounds, genders (m/f/d), and walks of life to join our team, as we believe that this fosters an inspiring workplace and fuels innovation. Our commitment to openness, trust, and diversity is at the heart of our mission to create digital futures that benefit businesses, societies, and humanity.
Diversity, equity and inclusion (tietoevry.com)