Responsibilities
- Design, develop, and maintain machine learning pipelines.
- Collaborate with data scientists and engineers to implement ML models.
- Ensure the scalability and reliability of ML systems.
- Optimize ML workflows for efficiency and performance.
- Monitor and troubleshoot ML infrastructure issues.
- Implement CI/CD pipelines for ML models.
- Develop and maintain data processing and transformation scripts.
- Ensure data security and compliance with regulations.
- Document ML processes and best practices.
- Provide technical support and guidance to team members.
- Stay updated with the latest trends and technologies in MLOps.
- Conduct performance testing and optimization of ML models.
- Integrate ML models with existing systems and applications.
- Collaborate with cross-functional teams to define project scope and objectives.
- Develop and maintain monitoring and alerting systems for ML models.
- Ensure the reproducibility and version control of ML experiments.
- Conduct code reviews and ensure adherence to coding standards.
- Participate in on-call rotations for ML infrastructure support.
- Develop and maintain dashboards for ML model performance metrics.
- Collaborate with stakeholders to understand business requirements.
- Provide technical leadership and mentorship to junior team members.
Nice to Have
- Experience with Spark and PySpark.
- Knowledge of MLOps best practices and standards.
- Experience with data warehousing and ETL processes.
- Familiarity with machine learning model deployment and serving.
- Experience with cloud-native architectures and microservices.
- Knowledge of data lakes and data warehouses.
- Experience with real-time data processing and streaming.
- Familiarity with A/B testing and experimentation frameworks.
- Experience with feature engineering and data preprocessing.
- Knowledge of machine learning model interpretability and explainability.
Compensation
Competitive salary
Work Arrangement
Remote
Team
Dynamic team
Technical Stack
- Databricks
- AWS
- Python
- Scala
- Docker
- Kubernetes
- Git
- Spark
- PySpark
What We Offer
- Competitive salary
- Remote work
- Dynamic team
- Opportunities for professional growth
- Collaborative work environment
How to Apply
- Submit your resume and cover letter
- Include relevant experience and skills
- Highlight your expertise in Databricks and AWS
- Describe your experience with MLOps and machine learning
- Provide examples of your problem-solving abilities
Not specified
