Everfield - AI Engineer

• Data Science & Machine Learning • Solid foundation in mathematics and statistics. • Expert knowledge of machine learning models and analytical/mathematical modeling. • Statistical knowledge of Time Series and forecasting evaluation metrics. • Advanced proficiency in Python (OOP best practices, testing, Pandas, PySpark, NumPy, • Scikit-learn, LightGBM / XGBoost / Catboost, Matplotlib, TensorFlow, Hugging Face). • Graph analysis and algorithm development. • Generative AI • Knowledge of the state-of-the-art in Language Models and GenAI. • Agentic AI Architecture: agent loops, tools, MCPs, and Python agentic libraries. • Ability to deploy agents, build RAG systems, and extraction techniques using • visual/computer vision models. • Data Engineering & Big Data • Strong mastery of SQL and query optimization. • Knowledge of the Big Data ecosystem (Spark, Glue, Redshift, etc.). • Airflow: Ability to build DAGs to orchestrate data flows. • Experience handling Big Data file formats such as Parquet and DuckDB. • Pipeline design and ETL architecture (scheduling, backfilling management, fault recovery, • Infrastructure & Operations (MLOps) • Knowledge of AWS and basic data infrastructure (S3, Redshift, Bedrock, EC2, EKS, ECR). • Docker: Containerization for service deployment. • Kubernetes: Ability to operate and interact with clusters. • Scalable architectures for ML operations. • Proficiency with GIT version control. • Experience putting models into production. • Continuous monitoring and model retraining for live operations. • Business Intelligence • Knowledge of data visualization tools (Apache Superset, Power BI). • Dashboarding: Ability to create dashboards for business stakeholders.