Assist in the design, implementation, and optimization of AI models for distributed systems.

Work with cloud computing platforms (e.g., AWS, GCP, Azure) to scale AI workloads.

Collaborate with the team to integrate AI algorithms into distributed architectures.

Develop and optimize code for high-performance AI systems and algorithms.

Assist in the evaluation and testing of AI models in distributed environments.

Work with large datasets, ensuring efficient data storage and processing across nodes.

Help improve AI model performance, scalability, and fault tolerance in distributed settings.

Contribute to documentation, code reviews, and research papers (if applicable).

Currently pursuing or recently completed a degree in Computer Science, Information Technology, or a related field. 

Basic understanding of AI/ML algorithms and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).

Familiarity with distributed computing concepts and frameworks (e.g., Spark, Hadoop, MPI).

Proficiency in programming languages such as Python, Java, or C++.

Understanding of cloud platforms and distributed storage systems.

Strong problem-solving and analytical skills.

Experience with containerization and orchestration tools like Docker and Kubernetes.

Knowledge of parallel and distributed computing techniques.

Familiarity with data parallelism, model parallelism, and distributed training of AI models.

Experience with high-performance computing (HPC) clusters or cloud-based AI environments.

Exposure to advanced AI topics like reinforcement learning, deep learning, or natural language processing.

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      Hands-on experience with cutting-edge distributed AI technologies and projects.

     Mentorship from experienced AI researchers and engineers.

     Flexible working hours and the option to work remotely.

  Certificate of Internship and Letter of Recommendation upon successful completion.

Opportunity for a full-time role based on performance