Strong foundation in classical ML algorithms and model development.
Experience with ML lifecycle management including:
MLflow for experiment tracking (experiments, runs, artifacts, parameters, model versioning).
Understanding of how experiments are judged and maintained.
Model production monitoring
Hands-on work in ML model deployment and evaluation preferred.
2. Data Engineering (Secondary Focus)
Experience with Snowflake and Databricks.
Ability to work with data pipelines, ETL/ELT workflows, and data transformation.
3. Generative AI
Exposure to GenAI concepts and tools.
Familiarity with LLMs (Large Language Models) and prompt engineering is a plus.
While not the top priority, the candidate should grasp the fundamentals of GenAI.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: IT & Information SecurityRole Category: IT & Information Security - OtherRole: IT & Information Security - OtherEmployement Type: Full time