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MLOps Engineering Lead
Becton Dickinson
posted 2mon ago
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Flexible timing
Key skills for the job
Job Description
- Pipeline Design: Architect and implement scalable and robust MLOps pipelines on Azure Databricks.
- Data Management: Develop data ingestion, transformation, and preprocessing pipelines to ensure clean and structured data for model training.
- Model Training: Set up automated workflows for model training, hyperparameter tuning, and validation.
- Model Deployment: Implement solutions for deploying models into production environments, ensuring seamless integration with existing systems.
Automation and Integration:
- End-to-End Automation Automate the entire ML lifecycle from data ingestion to model deployment and monitoring.
- CI/CD Pipelines: Develop and manage CI/CD pipelines for ML models using tools like Azure DevOps, Jenkins, or GitHub Actions.
- Integration: Collaborate with data scientists, data engineers, and software developers to integrate ML models into production applications.
Monitoring and Maintenance:
- Performance Monitoring: Implement monitoring solutions to track model performance, accuracy, and drift.
- Scalability: Ensure models are scalable and can handle increased load and data volume.
- Issue Resolution: Diagnose and resolve issues in ML pipelines promptly to minimize downtime and maintain high availability.
Documentation and Best Practices:
- Documentation: Create and maintain detailed documentation for ML Ops processes, pipelines, and best practices.
- Best Practices: Stay up to date with the latest advancements in ML Ops and Azure Databricks and incorporate new techniques and tools into the workflow.
Security and Compliance:
- Security Measures: Ensure that all ML workflows adhere to security standards and compliance requirements.
- Data Privacy: Implement data privacy and protection measures throughout the ML lifecycle.
Technical Skills:
- Proven experience as an MLOps Engineer or similar role, with a strong focus on Azure Databricks.
- Azure Expertise: In-depth knowledge of Azure services, particularly Azure Databricks, Azure Machine Learning, Azure Data Factory, and other related services.
- Programming: Proficiency in Python and SQL. Familiarity with other programming languages such as Scala or R is a plus.
- ML Lifecycle Tools:Experience with ML lifecycle management tools such as MLflow, Kubeflow, or similar platforms.
- Containerization: Proficiency in containerization technologies like Docker and orchestration with Kubernetes.
- CI/CD Practices: Strong understanding and experience with CI/CD practices and tools, such as Jenkins, GitHub Actions, or Azure DevOps.
- Data Engineering: Solid understanding of data engineering principles and experience with ETL processes.
- Big Data Technologies: Familiarity with big data technologies like Apache Spark and Hadoop.
Soft Skills:
- Problem-Solving: Excellent analytical and problem-solving skills, with a keen attention to detail.
- Communication: Strong verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
- Collaboration: Demonstrated ability to work effectively in a collaborative, cross-functional team environment.
Preferred Qualifications:
- Degree: Bachelor s or Master s degree in Computer Science, Engineering, Data Science, or a related field.
- 7 to 9 years of working experience. 3 to 5 years of experience as ML Ops Engineer
- Additional Cloud Experience: Experience with other cloud platforms such as AWS or Google Cloud Platform.
- Certifications: Relevant certifications in Azure or related technologies (e.g., Microsoft Certified: Azure Data Scientist).
- Advanced ML Techniques: Experience with advanced machine learning techniques and frameworks (e.g., deep learning, reinforcement learning).
Benefits:
- Competitive Salary: Attractive salary package based on experience and qualifications.
- Professional Development: Opportunities for professional development, training, and certifications.
- Work-Life Balance: Flexible working hours and remote work options.
- Inclusive Environment: An inclusive and collaborative work culture that values diversity and innovation.
- Paid Time Off: Generous paid time off, including vacation days, sick leave, and holidays.
Employment Type: Full Time, Permanent
Functional Areas: Software/Testing/Networking
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Lead Engineer salary at Becton Dickinson
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What Becton Dickinson employees are saying about work life
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Flexible timing
Monday to Friday
No travel
Day Shift
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