AI/ML

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

Education: Bachelor of Engineering

Preferred skills: Technology->AI-Generative AI->Prompt Engineering,Technology->AI-AI Engineering->AI/ML Solution Architecture and Design,Technology->AI-AI Engineering->MLOps