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AI / Machine Learning Engineering Lead

Unifonic
Cairo, Egypt
Posted 2 months ago
6Applicants for1 open position
  • 1Viewed
  • 0In Consideration
  • 0Not Selected
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Job Details

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Job Description

The Engineering team at Unifonic is looking for a proactive and dynamic Machine Learning Engineering Lead, to join our diverse team of developers. In this role, you’ll be playing an active part in the hands-on process of building Machine Learning models and putting them into production, managing a team, and contributing to production-facing code on a regular basis.

The successful candidate should have a strong technical background in order to be a good counsel and advocate for engineering. They should also have excellent team management and leadership skills. The responsibilities of the Machine Learning Engineering Lead, include, but are not limited to:

  • Design and lead the implementation of AI/ML/NLP pipelines from ideation to production
  • Apply best practices for ML datasets selection, processing, and model training and testing.
  • Ensure the delivery of high-performance ML models in production by applying best practices of system design and backend APIs implementation.
  • Support with recruitment & hiring of team talents and facilitate their onboarding.
  • Work with product managers, designers, and engineers to prioritize, plan, and schedule work.
  • Communicate status, risks, and requirements to other groups.
  • Provide technical and non-technical guidance to the team, both individually and as a group.
  • Facilitate continuous learning and improvement for the team and its members.

Job Requirements

MUST HAVE:

  • Hands-on 10+ years of relevant engineering work experience and 4+ years of hands-on technical management experience in shipping Machine Learning Models and large-scale projects with multiple dependencies across teams.
  • Bachelor’s degree in a related field. (e.g. Computer Science, Computer Engineering, … etc)
  • Expert in Python with experience in common data science toolkits, such as NumPy, Pandas, PySpark, Scikit-Learn, Tensorflow, PyTorch, Keras, rasa, BERT, spaCy.
  • Hands-on experience in NLP is mandatory; e.g. Text representation (n-grams, a bag of words, TF-IDF, etc), feature extraction, part of speech tagging and recognition, text classification, Named Entity Recognition (NER), semantic extraction techniques, Machine Translation, slot filling, Sentiment analysis, etc.
  • Familiarity with MLOps best practices, e.g. Model deployment and reproducible research
  • Mastering data science needed skills like SQL, hypothesis testing, Data cleansing, data augmentation, data pre-processing techniques, dimensionality reduction, mathematics, probability, and statistics (e.g. conditional probability, likelihood, Bayes rule, and Bayes nets, Hidden Markov Models, etc.).
  • Excellent understanding of Machine learning techniques like Naive Bayes classifiers, SVM, Decision Tree, KNN, K-means, Random Forest, modeling and optimization, evaluation metrics, classification, and clustering.
  • Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, etc.
  • Experience with data visualization tools, such as Grafana, Tableau, matplotlib, and seaborn.
  • Proficiency in using query engines and languages such as SQL, Hive, and Pig.
  • Experience with NoSQL databases, such as MongoDB, Cassandra, and HBase.
  • Solid understanding of fundamental design patterns and principles required for building scalable applications composed of reusable components using Python.
  • Solid experience with full Software Development Lifecycle, Distributed Architectures (REST, SOAP, Queue-based), and Microservices Architecture.
  • Familiarity with famous Python ORM (Object Relational Mapper) libraries
  • Ability to design systems that integrate with multiple data storage solutions including relational databases, key-value stores, and different cloud-based services.
  • Solid understanding and hands-on experience in concurrency patterns, and event-driven architecture.
  • Understanding of fundamental design principles behind a scalable cloud-based application, using containerization and Kubernetes.
  • Strong unit test and debugging skills.
  • Proficient understanding of code versioning tools such as GIT, CI/CD concepts, and toolchains.
  • Experience with the Agile Framework and Project Management tools such as Jira

 

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JobsIT/Software DevelopmentAI / Machine Learning Engineering Lead