Job Summary:
We are seeking a Machine Learning Engineer to develop and operationalize scalable ML models and feature engineering pipelines within ecosystem. This role focuses on production-grade ML systems, MLOps, and continuous model optimization.
Key Responsibilities:
- Design, develop, deploy, and optimize machine learning solutions.
- Build scalable feature engineering pipelines and ML workflows.
- Collaborate with data scientists, architects, and engineers on ML solutions.
- Develop and maintain model training, inference, and monitoring pipelines.
- Implement MLOps practices including model versioning and automated retraining.
- Conduct model evaluation, optimization, and monitoring activities.
- Troubleshoot issues across the ML stack and improve system reliability.
- Support production deployments and operational activities.
Required Skills & Experience:
- 4+ years of professional ML Engineering experience.
- Strong expertise in Python, PySpark, SQL, Scala, and Spark.
- Hands-on experience with ML frameworks including Scikit-learn, TensorFlow, PyTorch, XGBoost, and MLflow.
- Experience with Databricks, Delta Lake, and feature engineering tools.
- Knowledge of REST APIs using FastAPI or Flask.
- Familiarity with Docker, Kubernetes, and cloud ML platforms.
- Experience with monitoring tools such as Grafana and Azure Monitor.
Preferred Qualifications:
- Retail industry experience preferred.
- Knowledge of A/B testing and causal inference is a plus.
- Strong Agile delivery and communication skills.
Job Type: Contract
Contract length: 12 months
Application Question(s):
- Strong expertise in Python, PySpark, SQL, Scala, and Spark?
- Hands-on experience with ML frameworks including Scikit-learn, TensorFlow, PyTorch, XGBoost, and MLflow.
- Experience with Databricks, Delta Lake, and feature engineering tools.
Experience:
- Machine Learning : 4 years (Required)
Language:
Work Location: Remote
