World Wide Technology · 5 days ago
MLOps Engineer
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Responsibilities
Develop, productionize, and deploy scalable, resilient software solutions to integrate and operationalize AI & ML capabilities.
Adhere to software architecture and software design best practices to write scalable, maintainable, well-designed code.
Develop automated end-to-end ML delivery pipelines; enabling model training, evaluation, tracking, and serving.
In collaboration with Data Engineering, design and build feature engineering pipelines for extraction, transformation, and loading of data from a variety of data sources for ML models.
Advocate for AI security and responsibility best practices. Enable monitoring and observability capabilities for the delivery teams.
Stay current on new developments in ML frameworks, tooling, data modelling, software architecture and libraries available for solution development.
Coach data scientists and data engineers on software development best practices.
Work in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, DevOps engineers, designers, product managers, technical delivery teams, and others to continuously innovate AI and MLOps solutions.
Act as a positive champion for broader organization to develop stronger understanding of software design patterns that deliver scalable, maintainable, well-designed analytics solutions.
Acts as an expert on complex technical topics that require cross-functional consultation.
Qualification
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Required
Experience applying continuous delivery best practices, including CI/CD, Version Control, Trunk Based Development, Release Management, and Test-Driven Development using popular tooling (e.g., Azure DevOps, CircleCI)
Experience deploying and operating scalable, reliable, and secure software solutions on modern clouds (AWS, Azure, GCP) and container platforms (Docker, Kubernetes, etc.)
Knowledge of Machine Learning lifecycle (wrangling data, model selection, model training, modeling validation, drift monitoring, and deployment at scale) and experience working with data scientists
Familiar with cloud data warehousing solutions such as Snowflake, BigQuery, Fabric, etc.
Experience with popular MLOps tools (e.g., Kubeflow, mlflow, AzureML, Domino)
Preferred
Experience with deep learning platforms (eg: Databricks, Nvidia DGX) and frameworks (e.g.: TensorFlow, Keras, Theano, PyTorch, Caffe, etc.)
Experience working in Data Architecture, engineering and ETL teams, managing implementation of projects that utilize big data, advanced analytics, and machine learning technologies
Experience with Edge Analytics, embedded systems, or computer vision
Experience influencing and building mindshare convincingly with any audience. Confident and experienced in public speaking to large audiences.
Demonstrated experience in teaching and/or mentoring professionals
Benefits
Health, Dental, and Vision Care
Onsite Health Centers
Employee Assistance Program
Wellness Program
Competitive Pay
Profit Sharing
401k Plan with Company Matching
Life and Disability Insurance
Tuition Reimbursement
PTO & Holidays
Parental Leave
Sick Leave
Military Leave
Bereavement
Nursing Mothers Benefits
Voluntary Legal
Pet Insurance
Employee Discount Program
Company
World Wide Technology
World Wide Technology is a systems integrator, provides information technology and supply chain solutions.
Funding
Current Stage
Late StageTotal Funding
$25M2000-02-13Series Unknown· $25M
Recent News
2024-04-09
2024-04-09
Company data provided by crunchbase