Machine Learning Engineer
4 weeks ago
About This Role
This position will be responsible for technical and development support for PowerSchool's award-winning K-12 software. The ideal candidate will help in all AI/generative AI products in the areas of engineering, data, deployment, and infrastructure.
Key Responsibilities
The following are the essential duties and responsibilities for this role:
- Design and implement machine learning models and data ingestion pipelines.
- Develop and support a platform that enables data scientists to rapidly develop, train, and experiment with machine learning models.
- Expand and optimize data pipelines, data flow, and collection for cross-functional teams.
- Create and maintain optimal data pipeline architecture by assembling large, complex data sets to meet functional and non-functional business requirements.
- Identify and implement internal process improvements, including automating manual processes, optimizing data delivery, and redesigning infrastructure for greater scalability.
- Support the building of machine learning, data platforms, and infrastructure required for optimal data extraction, transformations, and loading of data from a wide variety of data sources.
- Work with architecture, data, and design teams to assist with data-related technical issues and support data infrastructure needs.
- Deploy ML models in AWS environment specifically in AWS Sage Maker environment.
- Implement Model Monitoring, Data Quality Checks, Data Drifts in Inference Pipelines.
- Support ML teams in the delivery of continuous integration, continuous deployment, providing templates and patterns.
- Perform root cause analysis for production issues where the root cause is in infrastructure, environment, configuration, or deployment routines; understand when to escalate to product development teams; remediate root causes and implement preventative actions.
- Own the AWS stack which comprises all ML resources and collaborate on managing ML infrastructure costs.
- Establish standards and practices around MLOps, including governance, compliance, and data security.
- Use Generative AI models, other LLMs, and LangChains to build different smart solutions.
- Use customer management system to provide status on open customer issues and properly verify when an issue can be closed.
- Participate in after-hours maintenance, when necessary, respond to emergencies, participate in customer calls when called upon in support of initiatives and incident response.
Requirements
The qualifications for this role include:
- At least 5+ years of experience within the full software development lifecycle from planning through deployment and maintenance.
- Demonstrated ability to design, implement, and scale machine learning workflows (ML Ops); including deployment and delivery of production-ready model APIs.
- Demonstrated proficiency with version control systems and automated software testing and delivery.
- Proficiency with at least one machine learning lifecycle platform (Sagemaker, MLFlow, TensorFlow, etc.), orchestration platform (Airflow, Dagster, etc.), and data platform like SnowFlake/DataBricks.
- 5+ years of experience with ML infrastructure and ML DevOps.
- 5+ years of overall engineering experience in distributed systems and data infrastructure.
- 3+ years experience coding in Python (preferred) or other languages like Java, C#, etc.
- Experience working with ML engineers to build tooling and automation to support the entire ML engineering lifecycle, from experimentation to production operations.
- Experience with Kubernetes and ML CI/CD workflows.
- 3+ years experience with AWS or other public cloud platforms (GCP, Azure, etc.).
- Excellent verbal and written communication skills.
- Experience with Infrastructure-as-Code tools and frameworks.
- Bachelor's degree in computer science, data science, mathematics, or a related field. Master's degree preferred.
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