Responsible for the end-to-end lifecycle of machine learning systems including design, training, deployment, and MLOps best practices implementation.
We are seeking an experienced Machine Learning Engineer to join our team. The successful candidate will be responsible for the end-to-end lifecycle of machine learning systems, from designing, training, and deploying ML models for production use cases to developing robust data pipelines for preprocessing and serving. A strong focus will be on implementing MLOps best practices, including CI/CD, monitoring, and model lifecycle management
Responsibilities
- Design, train, and deploy machine learning models for production use cases.
- Develop data pipelines for preprocessing, feature engineering, and model serving.
- Implement MLOps best practices: CI/CD for ML, monitoring, retraining workflows, and model lifecycle management.
- Build and validate ML Proof of Concepts (POCs) for new business opportunities.
- Optimize models for performance, scalability, and efficiency (latency, memory, throughput).
- Collaborate with data engineers, DevOps, and product teams to ensure seamless integration of ML systems.
- Stay up to date with new ML/AI research and tools to bring innovation into projects.
Required Skills
- Proven experience building and deploying ML systems in production.
- Strong proficiency in Python (Pandas, NumPy, Scikit-Learn, XGBoost, PyTorch/TensorFlow, Jupyter).
- Knowledge of SQL, Spark and data manipulation.
- Familiarity with MLOps tools (MLflow, SageMaker, or Vertex AI).
- Hands-on experience with cloud platforms (AWS, GCP, or Azure).
- Strong understanding of software engineering best practices (testing, version control, CI/CD).
- Nice to have: experience with feature stores, time series modeling, or generative AI techniques.
Top Skills
AWS
Azure
GCP
Jupyter
Mlflow
Numpy
Pandas
Python
PyTorch
Sagemaker
Scikit-Learn
Spark
SQL
TensorFlow
Vertex Ai
Xgboost
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