Description

Key Responsibilities

Design, build, train, and maintain machine learning and deep learning models for real-world applications.

Develop and maintain scalable data pipelines for data collection, cleaning, transformation, and ingestion.

Conduct experiments and analyze model performance using metrics such as accuracy, recall, and AUC.

Optimize models for performance, scalability, and efficiency.

Work with structured data and develop solutions using SQL and data modeling techniques.

Collaborate with engineering and data teams to integrate ML solutions into production environments.

Apply sound software architecture and engineering principles to machine learning projects.

Required Qualifications

Strong proficiency in Python, including PySpark.

Solid understanding of software architecture and engineering principles.

Hands-on experience with machine learning frameworks such as Scikit-learn.

Strong foundation in statistics, probability, and algorithm design.

Experience with SQL, data modeling, and data pipeline development.

Strong analytical and problem-solving skills.

Ability to work effectively in a collaborative, fast-paced environment.

Preferred Qualifications

Experience with MLOps and production model deployment.

Experience using Docker for deploying machine learning models.

Familiarity with deploying and working with local Large Language Models (LLMs).

Experience with scalable ML systems and cloud-based environments

Key Skills

Education

Bachelor’s degree