N-iX
SeniorDataScienceEngineer
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“Senior Data Science Engineer at N-iX”
Industry & Context.
Problem-solving
How You'll Work.
Team & Collaboration
Data Engineers; Backend Engineers; Product Managers; Domain Experts; Engineering teams; Cross-functional teams
Communication Scope
Communication skills
Process & Methodology
Agile, Sprint-based delivery
Full Job Description
N-iX is a global software development company founded in 2002, connecting over 2,400+ tech professionals across 40+ countries. We deliver innovative technology solutions in cloud computing, data analytics, AI, embedded software,IoT, and more to global industry leaders and Fortune 500 companies. Join us to create technology that drives real change for businesses and people across the world. Our client is an innovative technology company developing advanced AI-powered solutions for enterprise and industrial environments. The organization focuses on leveraging artificial intelligence to optimize complex engineering, manufacturing, and supply chain processes through intelligent software platforms and data-driven decision-making. The role supports two strategic AI initiatives aimed at transforming engineering and supply chain operations. The first program focuses on accelerating systems engineering lifecycles through NLP, Large Language Models, and automated compliance checking of complex technical and regulatory documentation. The second program is dedicated to predictive supply chain intelligence, including demand forecasting, spend analytics, supplier risk assessment, and anomaly detection. The technical environment includes enterprise ERP systems, engineering data models, Data Lakehouse infrastructure, and large-scale AI platforms integrated through RESTful services. Responsibilities Design, develop, and deploy production-grade machine learning and AI solutions. Fine-tune and optimize Large Language Models (LLMs) for domain-specific use cases involving technical and regulatory documentation. Build NLP pipelines capable of extracting structured rules and business logic from unstructured text sources. Design and implement Retrieval-Augmented Generation (RAG) architectures for intelligent querying of large technical knowledge bases. Develop time-series forecasting models for spend prediction, demand planning, and supply chain optimization. Build machine learning models
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