Machine Learning Engineer

4 weeks ago


Bengaluru, Karnataka, India PowerSchool Full time

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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