AI/ML ENGINEER
$200,000–$200,000 year
On-siteMaitama, Federal Capital Territory, Federal Republic of Nigeria
Job Summary
Design, develop, and deploy machine learning models for real-world applications while analyzing large datasets to extract insights and engineer features. Experiment with supervised, unsupervised, and deep learning algorithms to optimize performance, scalability, and efficiency. Collaborate with data engineers, product teams, and software developers to translate business problems into data-driven solutions. Monitor and maintain model performance and retraining pipelines in production, documenting processes for reusability and transparency. Requires a Bachelor's or Master's degree, three years of experience, and proficiency in Python, TensorFlow, or PyTorch. Familiarity with cloud platforms like AWS, Azure, or GCP is essential.
Required Qualifications
- at least 3 years of experience
- proficient in data preprocessing, model building, evaluation, and deployment
- strong foundation in algorithms and software engineering
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field
- Minimum of 3 years of experience in AI/ML model development and deployment
- Proficiency in Python
- experience in ML libraries (e.g., scikit-learn, TensorFlow, PyTorch)
- Strong understanding of machine learning algorithms, neural networks, and statistical modeling
- Experience with data manipulation tools (e.g., Pandas, NumPy) and SQL
- Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and ML Ops tools (e.g., MLflow, Kubeflow)
- Solid understanding of software engineering practices including version control (Git), testing, and CI/CD pipelines
- Ability to communicate technical concepts clearly to non-technical stakeholders
Desired Qualifications
- Experience with natural language processing (NLP), computer vision, or recommendation systems
- Exposure to large language models (e.g., GPT, BERT) and generative AI tools
- Knowledge of model interpretability and fairness techniques
- Published research or contributions to open-source ML projects
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