Senior Advisor, Machine Learning Engineer – Egypt – Information Technology Company – ML-152
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Information Technology Company located in Cairo, Egypt looking for Senior Advisor, Machine Learning Engineer with the below requirements:
8+ years of related experience with a Bachelor’s degree (or equivalent experience), or 6+ years with a Master’s, or 3+ years with a PhD; Experience to include:
Software engineering experience in productionizing Machine Learning models, and scaling them in low-latency settings (C, C++, Python)
Primary experience with object-oriented programming languages such as C# or Java, and with Data Science tools & frameworks and data engineering tools (Python, Spark, Tensorflow, XGBoost…)
Data Science Platform knowledge – Deep knowledge of machine learning operations and cloud-native ecosystems, information retrieval, data mining, statistics, NLP or related field; Proficient in Data Mining, Data transformation, and Database building ( ETL, SQL OLAP, Teradata, Hadoop )
Microsoft Azure, AWS, and Google Cloud – must have hands-on experience working on cloud environments to build and deploy models.
Knowledge of cloud-native computing DevOps, data streaming and extraction, parallelized workloads using Docker, Kubernetes, Test Driven Development, and Continuous Integration / Continuous Deployment.
Creative Problem-solving approach, and excellent communication skills verbally, in writing, in presentations and meetings, with relationship building and teamwork success based on building trust.
Deep experience in machine learning and especially building solutions thru Neural networks is a plus.
Big Data experience in driving real-time analytics.
Build Data Pipelines & Platform to operationalize Machine Learning (ML) models at scale
Work with the data science team to enable robust decision-making in terms of thinking about scale, latency, and throughput requirements; and create tools/systems to speed up ML lifecycle
Play a significant role in enabling the adoption of sophisticated algorithms and data mining strategies
Generate new practices and processes for effective ML engineering, in alignment with core subject-matter expertise
Define best practices for code optimization, model and system validation, and model governance
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