AI/ML

Responsibilities

• Python, data structures, basic software engineering discipline (clean code, testing)
• ML fundamentals: supervised/unsupervised learning, metrics, bias/variance, evaluation
• Data handling: SQL, data quality, feature understanding
• Git, basic CI/CD awareness
• Cloud basics (any of AWS/Azure/GCP), containers (Docker) basics

Additional Responsibilities

• Distributed computing: Spark, Ray
• Data engineering familiarity: ETL/ELT, orchestration (Airflow/ADF)
• Security basics: secrets management, IAM concepts

Preferred Skills

• Prompt Engineering
• AI/ML Solution Architecture and Design
• MLOps

Educational Requirements

Bachelor of Engineering