Senior MLOps Engineer
7 days ago
A Snaphyre Client
Job Title: Senior MLOps Engineer
The role of Senior MLOps Engineer at A Snaphyre Client is a pivotal position in our team, responsible for overseeing the entire lifecycle of machine learning models. This includes design, implementation, deployment, and monitoring. Our ideal candidate should have expertise in cloud platforms, such as AWS, GCP, or Azure, and infrastructure as code tools, like Terraform or CloudFormation.
Key Responsibilities:
- Design and implement automated ML pipelines for model development, testing, deployment, and monitoring.
- Collaborate closely with Data Science and Engineering teams to integrate models into production environments.
- Develop infrastructure for model versioning, scaling, and serving to ensure high availability and low latency.
- Establish CI/CD processes for model deployment and data pipelines, ensuring reproducibility and consistency across environments.
- Monitor model performance, set up logging, and create alert systems for ML models in production.
- Optimize ML workloads for performance and cost-efficiency in cloud environments.
- Ensure data integrity, compliance, and security standards, especially concerning sensitive agricultural data.
- Participate in green computing initiatives to minimize the carbon footprint of ML operations.
Requirements:
- Bachelor's degree in Computer Science, Engineering, or a related field.
- 6–7 years of experience in IT, focusing on MLOps, DevOps, or Data Engineering roles.
- Expertise with cloud platforms (AWS, GCP, Azure) and infrastructure as code tools (e.g., Terraform, CloudFormation).
- Hands-on experience with Kubernetes or other container orchestration technologies for scaling ML models.
- Proficiency with CI/CD tools (e.g., Jenkins, GitLab CI, ArgoCD) and familiar with version control (e.g., Git).
- Experience with data pipeline orchestration tools like Apache Airflow or Kubeflow.
- Understanding of monitoring tools and techniques for tracking model performance (e.g., Prometheus, Grafana, ELK stack).
- Data management and ETL processes, particularly with agricultural and environmental data.
- Proficiency in Python, with exposure to ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
- Experience in the agriculture sector or climate tech, with knowledge of carbon/GHG emissions projects.
- Familiarity with geospatial data and remote sensing tools (e.g., Sentinel-2, Google Earth Engine) is a plus.
- Commitment to automation, efficiency, and sustainability in ML operations.
What We Offer:
- Work with a well-funded AgriTech startup tackling critical challenges in Agriculture and the Environment.
- Play a pivotal role in shaping a high-impact product driving the future of Agriculture.
- Work with state-of-the-art technologies in AI, remote sensing, and data analytics.
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