Senior ML Engineer

Overview
Design and lead the development of high-performance ML systems for predictive analytics, optimization, and anomaly detection  role involves architecting production-grade ML pipelines and managing multiple data modalities.

Job Description
Core Responsibilities
Architect end-to-end ML pipelines for high-frequency operational datasets.
Design automated retraining, drift detection, and feature store management.
Apply causal inference and probabilistic modeling for root cause analysis.
Mentor ML engineers and enforce best practices in reproducibility and testing.
Collaborate with business stakeholders to translate ML outcomes into actionable KPIs.
Required Technical Skills
Languages & Frameworks:
Python, SQL, scikit-learn, XGBoost, Light

GBM.
Time-Series: ARIMA, Prophet, LSTM.
Causal & Probabilistic Modeling:
PyWhy, Bayesian Networks.
Data Handling:
Pandas, Num Py, PySpark.
Evaluation:
Cross-validation, ROC, MAE, RMSE, SHAP/LIME explainability.
Advanced

Skills:

Ensemble modeling, AutoML, interpretability (SHAP, ELI5).
MLOps & Scaling:
Kubeflow, Airflow, MLflow, Docker, FastAPI, CI/CD pipelines.
Cloud & Compute:
Azure ML, AWS Sage Maker, or GCP Vertex AI.