Machine Learning Engineer 2
Machine Learning Engineer 2
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Job Description
Overview
This position under the general direction of the Lead and/or Manager Machine Learning Engineering will be responsible for technical and development support for our awardwinning K12 software. This role will help in all AI/generative AI products in the areas of engineering data deployment andinfrastructure.
Responsibilities
Description
Essential duties and responsibilities include the following. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions
- 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 nonfunctional 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 aroundMLOps including governance compliance and data security
- Uses Generative AI models (GPT4 Claude Llama) other LLMs Agents and LangChainsto build different smart solutions
- Uses customer management system to provide status on open customer issues and properly verifies when an issue can be closed
- Participate in afterhours maintenance when necessary respond to emergencies participate in customer calls when called upon in support of initiatives and incident response
Qualifications
To be considered for and to perform this job successfully an individual must be able to perform each essential duty and responsibility satisfactorily. The requirements listed below are representative of the knowledge skill and/or ability required.
Qualifications 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 productionready model APIs
- Demonstrated proficiency with version control systems and automated software testing and delivery
- Proficiency with at least one machine learning lifecycle platform SagemakerMLFlow TensorFlow etc. orchestration platform (AirflowDagster etc. and data platform likeSnowFlake/DataBricks
- 5 years of experience with ML infrastructure and ML DevOps
- 5 years of overall engineering experience in distributed systems and data infrastructure
- 3years experiencecoding 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
- 3years experiencewith AWS or other public cloud platforms (GCP Azure etc.
- Excellent verbal and written communication skills.
- Experience with InfrastructureasCode tools and frameworks
- Bachelors degree in computer science data science mathematics or a related field. Masters degree preferred
EEO Commitment
EEO Commitment
PowerSchool is committed to a diverse and inclusive workplace. PowerSchool is an equal opportunity employer and does not discriminate on the basis of race national origin gender gender identity sexual orientation protected veteran status disability age or other legally protected status. Our inclusive culture empowers PowerSchoolers to deliver the best results for our customers. We not only celebrate the diversity of our workforce we celebrate the diverse ways we work. If you have a disability and need an accommodation regarding our recruiting process please let us know by emailing
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Employment Type
Full-Time
