About Snapp
Snapp is the pioneer provider of ride-hailing mobile solutions in Iran that connects smartphone owners in need of a ride to drivers who use their private cars to offer transportation services. We are ambitious, passionate, engaged, and excited about pushing the boundaries of the transportation industry to new frontiers and being the first choice of each user in Iran

About Role:
As a Machine Learning Engineer at Snapp, you will play a pivotal role in our operations. You will be instrumental in advancing our Deep Learning models, particularly in developing and maintaining Multilayer Perceptron (MLP) models for drivers and offerings. Your contributions to research and development efforts focused on Large Language Models (LLM) and Specialized Language Models (SLM) will be valuable. You will also be key in enhancing our MLOps infrastructure, ensuring the efficient deployment, monitoring, and scalability of our machine learning solutions. This role is critical for driving technological innovation, improving model performance, and optimizing our operational workflows.

Key Responsibilities:

• Maintain and optimize deep neural network models.
• To streamline processes, utilize tools such as Airflow, MLFlow, and other MLOps frameworks.
• Contribute to the development and fine-tuning of high-quality open-source LLM models.
• Design, conduct, and evaluate experiments to assess model outcomes.
• Deploy scalable machine learning solutions on Kubernetes.
• Recommend, develop, and implement strategies to enhance data reliability, efficiency, and quality.

Support the translation of business needs into technical system requirements.

• Proficiency in Python.
• Expertise in machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Experience with PySpark and Airflow.
• Strong knowledge of Docker, Kubernetes, and Helm for containerization and orchestration.
• Familiarity with CI/CD pipelines and A/B testing.
• Proficient in SQL (PostgreSQL) for data management and querying.

Preferred Qualifications:

Experience with Go Lang.

Familiarity with LangChain.

Knowledge of GPU utilization in Kubernetes environments.

Experience with Large Language Models (LLM) and Specialized Language Models (SLM).