Mid AI and Machine Learning Engineer

- Proven experience in developing, deploying, and managing production ML models, including supervised, unsupervised, and anomaly detection methods, especially for imbalanced datasets - Proficiency in ML engineering and MLOps, encompassing model versioning, CI/CD processes, monitoring, drift detection, and automated retraining - Strong skills in Python and ML frameworks such as scikit-learn, PyTorch, or TensorFlow - Knowledgeable in Palantir and data engineering tools like Databricks, Spark, or SQL, with experience in batch and streaming pipelines - Expertise in enhancing data quality, lineage, and observability in enterprise data systems, along with operationalizing rules and model-driven insights for prioritization and routing - Familiarity with API-first and event-driven integration strategies, including secure service-to-service communication - Understanding of responsible AI principles, focusing on explainability, fairness, and bias assessment - Competence in designing and drafting architectural artifacts, including data contracts and operational runbooks - Capability to secure and maintain a Public Trust or Suitability/Fitness determination based on client needs - A Bachelors degree with a minimum of 2 years of experience in DevOps, software, or data engineering, or 5 years of experience in similar fields if lacking a degree