inferencetekniker

Inferencetekniker is a professional role in data science and engineering focused on designing, implementing, and maintaining systems that perform inference. Inference refers to applying a trained model or statistical method to new data in order to produce predictions, decisions, or estimates. The role sits at the intersection of data processing, software engineering, and AI deployment, translating research models into reliable, production-ready capabilities.

Typical responsibilities include building and optimizing end-to-end inference pipelines, deploying models to production, and ensuring low latency, scalability, and reliability. A inferencetekniker selects appropriate hardware and software targets (cloud, on-premises, or edge devices), performs model optimization such as quantization or pruning, and monitors performance, drift, and failures. They also handle versioning, rollback strategies, incident response, and collaboration with data scientists to validate model outputs and improve accuracy.

Key skills for the role often encompass programming in Python, C++, or Java; experience with ML frameworks (such as TensorFlow, PyTorch, or ONNX); and knowledge of deployment tools (Docker, Kubernetes, or serverless platforms) and ML tooling (model registries, serving frameworks, and monitoring). A solid foundation in statistics or probabilistic reasoning, data engineering concepts, and familiarity with MLOps practices are common prerequisites. Privacy, bias mitigation, and ethical considerations are increasingly part of the standard remit.

Inferencetekniker work across industries that rely on data-driven decisions, including technology, finance, healthcare, and manufacturing. While the title may vary by country or company, the role is typically aligned with ML deployment engineering, AI infrastructure, or data engineering, emphasizing reliable, scalable, and explainable inference in real-world applications.