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.

Responsibilities:

  • 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