Machine Learning

3 weeks ago

Nairobi, Kenya Corporate Staffing Services Full time

About Us

We are the leading telecommunication company in East Africa. Our purpose is to transform lives by connecting people to people, people to opportunities and people to information. We keep over 42 million customers connected and play a critical role in the society, supporting over one million jobs both directly and indirectly while our total economic value was estimated at KES 362 Billion ($ 3.2 billion) for the 12 months through March 2021. We are listed on the Nairobi Securities Exchange (NSE) and with annual revenues of close to KES 298 Billion ($2.5 billion) as at March 2022. We were founded in 1997 as a fully owned subsidiary of Telkom Kenya before a 40 percent acquisition by Vodafone Group PLC in May 2000, and a public offering of 25 percent shares through the NSE in 2008. Under the management of Vodafone Group PLC, we welcomed Michael Joseph, as our first CEO, a few months later in July of 2000. He led the company’s growth to accommodate 16.71 million subscribers from the previous 20,000, largely owing to innovative products like M-PESA in 2007.

Responsibilities

Collaborate with data scientists and software engineers to design and implement machine learning workflows. Take offline models data scientists build and turn them into a real machine learning production system. Develop and deploy scalable custom tools and services that can handle machine learning training and inference Apply software engineering best practices to machine learning such as CI/CD, versioning and Containerization Develop machine learning algorithms and libraries for problem solving and AI operations. Research and provide input on design approach, performance and base functionality improvements for various software applications. Stay up to date with the latest developments in machine learning and cloud computing technologies.

Qualifications

BS or MS in computer science or equivalent practical experience At least 2-3 years of coding experience in a non-university setting. Proven experience in Object Oriented development Experience in deploying and managing Machine Learning models at scale Experience with MLOps platforms such as Kubeflow, MLFlow, Sagemaker etc. Familiarity with DevOps practices and tools such as Kubernetes, Docker, Jenkins, Git. Proficient understanding of distributed computing principles Experience with NoSQL databases, such as HBase, Cassandra, MongoDB Demonstrated proficiency with data structures, algorithms, distributed computing, and ETL systems. Good knowledge of and experience with big data frameworks such as Apache Hive, Spark Strong understanding of machine learning concepts and frameworks, including TensorFlow, PyTorch, Scikit-learn, Kedro etc.

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