
Machine Learning Model Developer
4 days ago
Mission Overview:
- We seek a highly skilled MLOPS Specialist to drive business value by developing, deploying, and maintaining machine learning models.
Key Responsibilities:
- Develop predictive and analytical models using machine learning techniques and train them on large datasets.
- Collaborate with data engineers and business teams to define data requirements and success metrics for model deployment.
- Deploy ML models into production using industry best practices such as continuous integration/continuous delivery (CI/CD).
- Design automated pipelines for model training, testing, monitoring, and retraining to ensure model performance and scalability.
- Optimize model performance, handle model drift, and ensure continuous improvement of machine learning workflows.
- Implement CI/CD practices and version control systems for reproducibility and collaboration.
Technical Skills Required:
- Strong proficiency in Python libraries (Pandas, NumPy, Scikit-learn, PyTorch/TensorFlow) and experience with ML Ops tools (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure ML).
- Good understanding of data engineering concepts (ETL, data pipelines, APIs) and hands-on experience with containerization and orchestration (Docker, Kubernetes).
- Knowledge of cloud platforms (AWS, GCP, or Azure) for ML model deployment and strong background in statistics, machine learning algorithms, and model evaluation techniques.
- Familiarity with version control systems (Git) and CI/CD pipelines.
Preferred Qualifications:
- Bachelor's/Master's degree in Computer Science, Data Science, Statistics, or related field.
- Experience in handling large-scale data pipelines and real-time model serving.
- Exposure to feature stores and automated model monitoring.
- Strong problem-solving, analytical, and communication skills.
Job Requirements:
- Develop and maintain high-quality machine learning models that meet business needs.
- Collaborate with cross-functional teams to integrate ML models into production environments.
- Design and implement automated pipelines for model training, testing, and deployment.
- Optimize model performance and ensure scalability, reliability, and continuous improvement.
- Stay up-to-date with industry trends and advancements in machine learning and MLOPS.
Benefits:
- Competitive salary and benefits package.
- Opportunities for professional growth and development.
- Collaborative and dynamic work environment.
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