Must Have Technical/Functional Skills
MLOps & Deployment:
CI/CD for ML, MLflow/Kubeflow/SageMaker/Azure ML
Model versioning, monitoring, retraining workflows
Docker, Kubernetes for deployment
Programming & Cloud:
Strong Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow)
Experience on AWS/Azure/Google Cloud Platform ML services
Roles & Responsibilities
Looking for a strong hands-on ML Engineer with deep experience in data science, model development, and building scalable data/ML pipelines. Candidate must be technically solid, execution-focused, and able to deliver production-ready ML solutions.
Core Technical Skills Needed
ML Model Development:
Supervised models: Logistic Regression, Random Forest, XGBoost/LightGBM, SVMs
Deep learning: Neural Networks, CNN/RNN, Transformers (PyTorch or TensorFlow) Classification models and evaluation techniques (AUC, ROC, precision/recall, cross-validation)
Data Preparation & Feature Engineering: Data cleaning, handling missing values & outliers Feature scaling, encoding, time-series feature generation Strong EDA and statistical analysis skills
Data Pipeline Engineering: Build and maintain ML/data pipelines using Spark, Databricks, Airflow Data ingestion, transformation, validation (Great Expectations preferred)
MLOps & Deployment: CI/CD for ML, MLflow/Kubeflow/SageMaker/Azure ML
Model versioning, monitoring, retraining workflows
Docker, Kubernetes for deployment
Programming & Cloud: Strong Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow)
Experience on AWS/Azure/Google Cloud Platform ML services
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