#weAreHiring 

JOB TITLE: AI/ML ENGINEER (Africans) 

🌍 Location: Remote
⏰ Hours: 5AM-1 PM EST
⏲️ Deadline: July 2, 2025
💰 Compensation: $600-$800 depending on experience

Love the challenge? Apply here: https://lnkd.in/enQp-s_Z

Learn more about this vacancy here: https://lnkd.in/dY2-DQmC

JOB DESCRIPTION

EBO is seeking a skilled and innovative AI/ML Engineer to design, build, and deploy intelligent
systems that drive real business value. You’ll collaborate across engineering, product, and data
teams to develop machine learning models and integrate them into scalable systems. This is an
exciting opportunity to shape our AI strategy and apply cutting-edge techniques to solve
real-world problems.

Key Responsibilities:
● Design, develop, and deploy machine learning models for [e.g., classification, regression,
NLP, computer vision, recommendation, etc.]
● Build and maintain scalable data pipelines for model training and inference
● Research and implement state-of-the-art ML/AI algorithms
● Collaborate with product managers and domain experts to define ML-based solutions
● Optimize models for performance, accuracy, and scalability
● Monitor model drift and re-train as needed
● Maintain documentation and contribute to AI/ML best practices within the team
● Participate in code reviews, architecture discussions, and tech strategy

Required Qualifications:
● Bachelor's or Master’s degree in Computer Science, Data Science, Mathematics, or
related field
● 3+ years experience in machine learning model development and deployment
● Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch,
XGBoost, or HuggingFace● Strong grasp of statistics, probability, and algorithm design
● Experience with cloud platforms (AWS, GCP, or Azure) and ML deployment (e.g.,
SageMaker, Vertex AI, MLflow)
● Familiarity with data processing tools (e.g., Pandas, NumPy, Spark)
Nice-to-Have:
● Experience with MLOps (CI/CD, model versioning, monitoring)
● Knowledge of deep learning architectures (CNNs, RNNs, transformers)
● Exposure to real-time ML systems or edge deployment
● Experience with Kubernetes, Docker, or API development (Flask, FastAPI)

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