AI/ML Engineer at IndSAfri

Key Responsibilities

NLP Model Development & Deployment

• Design, train, and deploy NLP models for a variety of tasks, such as text classification, sentiment analysis, named entity recognition, and language generation.
• Leverage and fine-tune large language models (e.g., BERT, GPT, T5) for production use cases, ensuring efficiency and scalability.
• Develop end-to-end NLP pipelines to streamline text processing, model training, and evaluation in production.

Model Development & Deployment

• Develop, train, and optimize machine learning models using industry-standard frameworks such as TensorFlow, PyTorch, and Scikit-Learn.
• Design and implement end-to-end ML pipelines to streamline data ingestion, model training, and evaluation.
• Deploy machine learning models to production environments, ensuring robustness, scalability, and performance.

Data Preparation & Engineering

• Collaborate with data engineers to collect, clean, and preprocess data for training and evaluation purposes.
• Work with large datasets, employing data engineering skills to handle data pipelines and structure data in ways that are conducive to model accuracy and efficiency.
• Utilize big data technologies (e.g., Hadoop, Spark) to manage and analyse large volumes of data.

Model Monitoring & Maintenance

• Monitor models in production to ensure optimal performance, implementing retraining pipelines as necessary.
• Troubleshoot, tune, and update models based on feedback from stakeholders, data drift, or changes in underlying data patterns.

Collaboration & Communication

• Work closely with data scientists, software engineers, and product teams to align AI models with business goals.
• Translate complex technical findings into actionable insights for non-technical stakeholders.

Research & Development

• Stay updated with the latest advancements in AI/ML technology and best practices, applying relevant developments to current projects.
• Experiment with new algorithms, techniques, and tools that can improve model performance and solve new challenges.

Key Skills & Qualifications

• Educational Background: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field.

Technical Skills:

• Proficiency in Python and experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Keras).
• Solid understanding of ML algorithms and techniques (e.g., supervised/unsupervised learning, deep learning, reinforcement learning).
• Experience with data processing libraries (e.g., Pandas, NumPy) and visualization tools (e.g., Matplotlib, Seaborn).
• Familiarity with big data technologies (e.g., Hadoop, Spark) and database systems (SQL and NoSQL).

Mathematics & Statistics: Strong foundation in linear algebra, calculus, probability, and statistics.

• Software Development: Knowledge of software engineering best practices, including code versioning, unit testing, and CI/CD pipelines.
• Problem-Solving & Analytical Skills: Ability to troubleshoot complex ML model issues and optimize for performance.
• Communication: Ability to convey complex technical information to non-technical stakeholders effectively.

Preferred Qualifications

• Familiarity with cloud platforms (AWS, Azure, Google Cloud) and ML services (e.g., SageMaker, Azure ML).
• Experience in natural language processing, computer vision, or other specialized AI domains.
• Knowledge of MLOps practices for model lifecycle management and scalability.
• Prior experience in a similar AI/ML engineering role.