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Data Science Architect
Birdeye
posted 5mon ago
Job Role Insights
Flexible timing
Key skills for the job
Job Description
- Design and implement scalable and robust ML infrastructure to support end-to-end machine learning workflows.
- Develop and maintain CI/CD pipelines for ML models, ensuring smooth deployment and monitoring in production environments.
- Collaborate with data scientists and software engineers to streamline the model development lifecycle, from experimentation to deployment and monitoring.
- Implement best practices for version control, testing, and validation of ML models.
- Ensure high availability and reliability of ML systems, including performance monitoring and troubleshooting.
- Develop automation tools to facilitate data processing, model training, and deployment.
- Stay up-to-date with the latest advancements in MLOps and integrate new technologies and practices as needed.
- Mentor junior team members and provide guidance on MLOps best practices.
- Bachelors/Masters degree in Computer Science, Engineering, or a related technical field with 7-10 years of experience.
- Experience in designing and implementing ML infrastructure and MLOps pipelines.
- Proficiency in cloud platforms such as AWS, Azure, or GCP.
- Strong experience with containerization and orchestration tools like Docker and Kubernetes.
- Experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI.
- Solid programming skills in Python and familiarity with other programming languages such as Java or Scala.
- Understanding of ML model lifecycle management, including versioning, monitoring, and retraining.
- Experience with infrastructure-as-code tools like Terraform or CloudFormation.
- Familiarity with data engineering tools and frameworks, such as Apache Spark, Hadoop, or Kafka.
- Knowledge of security best practices for ML systems and data privacy regulations.
- Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
- Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Knowledge of data visualization tools and techniques.
- Understanding of A/B testing and experimental design.
- Strong analytical and troubleshooting skills.
- Excellent communication and documentation skills.
- Experience with monitoring and logging tools like Prometheus, Grafana, or ELK stack.
- Knowledge of serverless architecture and functions-as-a-service (e.g., AWS Lambda).
- Familiarity with ethical considerations in AI and machine learning.
- Proven ability to mentor and train team members on MLOps practices
Employment Type: Full Time, Permanent
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