Job Description

Machine Learning Engineer – Nairobi, Kenya (6 Months Contract)

We are hiring on behalf of our client, a renowned IT corporation, for a Machine Learning Engineer to join their team on a contract basis. The successful candidate will work on innovative projects that integrate machine learning models with large language models (LLMs). The role focuses on enhancing natural language understanding in low-resource languages and leveraging domain-specific data to boost LLM capabilities. The ideal candidate will possess hands-on experience in building and deploying ML solutions, a deep understanding of machine learning principles, and a collaborative mindset to work effectively within a research-driven environment.

Key Responsibilities:

• Collaborate with machine learning researchers and software engineers to develop efficient AI training and deployment methods.
• Design, develop, and maintain ML pipelines and APIs that interface with LLMs and other data sources.
• Implement and optimize natural language processing tasks such as question answering and summarization.
• Conduct experiments and performance evaluations of multilingual ML models.
• Create technical documentation and adhere to compliance and data governance standards.
• Support the rapid prototyping and evaluation of ML models with scalable, production-ready code.

Qualifications:

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field.
• At least 3 years of experience in machine learning or a related field.
• Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Solid understanding of data structures, algorithms, and model optimization techniques.
• Experience with predictive analytics, statistical modelling, or data mining.
• Familiarity with LLMs and their application in NLP tasks such as summarization and question answering.
• Experience with automatic speech recognition (ASR) models is an added advantage.
• Ability to work both independently and collaboratively in a fast-paced research environment.

Tagged as: Hybrid