ML Ops Engineer
Durham, North Carolina, United States
Syngenta Seeds is one of the world’s largest developers and producers of seed for farmers, commercial growers, retailers and small seed companies. Syngenta seeds improve the quality and yields of crops. High-quality seeds ensure better and more productive crops, which is why farmers invest in them. Advanced seeds help mitigate risks such as disease and drought and allow farmers to grow food using less land, less water and fewer inputs.
Syngenta Seeds brings farmers more vigorous, stronger, resistant plants, including innovative hybrid varieties and biotech crops that can thrive even in challenging growing conditions.
Syngenta Seeds is headquartered in the United States.
The ML OPS Engineer will be responsible for designing, implementing, and maintaining machine learning infrastructure, pipelines, and workflows. This role will require a deep understanding of data management, software development, and cloud computing. The successful candidate will work closely with data scientists, software engineers, and other stakeholders to ensure that machine learning models are deployed, monitored, and updated efficiently and effectively
- Develop, deploy and maintain machine learning models, pipelines and workflows in production environment.
- Build and maintain machine learning infrastructure that is scalable, reliable and efficient.
- Collaborate with data scientists and software engineers to design and implement machine learning workflows.
- Implement monitoring and logging tools to ensure that machine learning models are performing optimally.
- Continuously improve the performance, scalability and reliability of machine learning systems.
- Work with DevOps team to deploy and manage infrastructure for machine learning services.
- Create and maintain technical documentation for machine learning infrastructure and workflows.
- Stay up to date with the latest developments in machine learning and cloud computing technologies.
This person will work in our Durham, NC location in a hybrid work setting
- Bachelor's or Master's degree in computer science, engineering or related field.
- 5+ years of experience in software development, machine learning engineering or related field.
- Strong understanding of machine learning concepts and frameworks, including TensorFlow, PyTorch, Scikit-learn, etc.
- Experience with ML Ops in AWS preferred including Sagemaker.
- Familiarity with DevOps practices and tools such as Kubernetes, Docker, Jenkins, Git.
- Experience in developing and deploying machine learning models in a production environment.
- Strong analytical and problem-solving skills
- Experience with data lake technologies such as S3 and Snowflake
- Experience in a genetics/genomics life science setting
- Experience with time-series data and forecasting models.
- Experience with data streaming technologies such as Kafka, Kinesis, etc.
- Experience with MLOps platforms such as Kubeflow, MLFlow, Sagemaker etc.
- Familiarity with database technologies such as SQL, NoSQL, etc.
What we Offer
- A culture that celebrates diversity & inclusion, promotes professional development, and strives for a work-life balance that supports the team members
- We offer flexible work options to support your work and personal needs
- Full Benefit Package (Medical, Dental & Vision) that starts your first day
- 401k plan with company match, Profit Sharing & Retirement Savings Contribution
- Paid Vacation, 9 Paid Holidays, Maternity and Paternity Leave, Education Assistance, Wellness Programs, Corporate Discounts, among other benefits
Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status
Family and Medical Leave Act (FMLA)
Equal Employment Opportunity Commission's (EEOC)
Employee Polygraph Protection Act (EPPA)
* Salary range is an estimate based on our salary survey 💰
Tags: AWS Computer Science Data management DevOps Docker Engineering Genetics Git Kafka Kinesis Kubeflow Kubernetes Machine Learning MLFlow ML infrastructure ML models MLOps NoSQL Pipelines PyTorch SageMaker Scikit-learn Snowflake SQL Streaming TensorFlow
Perks/benefits: 401(k) matching Career development Flex vacation Health care Medical leave Parental leave
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