Principal Machine Learning Engineer

You will lead the charter to Industrialize AI/ML model development, feature engineering, Model validation, deployment and Model Observability in both Real Time and Batch setup.- Designing, developing, and deploying end-to-end AI/ML solutions, including data pipelines, model training, deployment, monitoring, and optimization

Deploying machine learning models – On Prem, Cloud and Kubernetes environments
Creating and implementing data and ML pipelines for model inference, both in real-time and in batches.
Architecting, designing, and implementing large-scale AI/ML systems in a production environment.
Leading the consolidation and implementation of new concepts and processes in areas including information retrieval, distributed computing, large-scale system design, networking, data storage, security, artificial intelligence, natural language processing, UI design, and mobile.
Setting the strategy for ML/AI tools and processes, determining the future needs of the business, and enhancing existing ML libraries and frameworks.
Analyzing extensive and complex data sets to determine the most efficient methods for processing large volumes of data using Spark, Hive, and SQL.
Monitor the performance of data pipelines and make improvements as necessary

What we’re looking for…

You are good with numbers and you love to dig into data to find the story. You are detail-oriented and know how to passionate about what really matters. You understand the importance of accuracy in data analytics and reporting. You are a self-starter and a multitasker who can work independently under tight timelines. You want to make an impact by providing the data that will help improve the experience of our customers.

You will need to have:

Bachelor’s degree or four or more years of work experience.
Six or more years of relevant work experience.
Strong experience in Spark/Hive/SQL, including hands-on experience building and deploying large volume data pipelines
Proficiency in Python, Scala, SQL PySpark, Kafka, use of scheduling tools, Devops using Jenkins
Experience cloud computing platforms (e.g., AWS, Azure, GCP) and their AI/ML services
Hands-on experience with ML Engineering techniques and tools, including ML Models measurement techniques, real-time and batch AI processors
Hands-on experience with modeling platforms

Even better if you have one or more of the following:

A degree in Computer Science, Engineering or related field.
Six or more years of Enterprise Architecture or platform management.
Hands-on experience with modeling platforms and tools such as and tools like Domino, Jupyter, H2O.ai, DataRobot, Conda, ML Flow
Advanced skills in programming in Python, Java, and Git using Open Source tools like Spark, Flink and Jupyter Notebook.
Knowledge of Development Lifecycle Management, DevOps Automation methods and practices, Post-Production Model Monitoring and End-to-End Architecture.
Ability to run workloads over multiple nodes.
Knowledge of data administration practices and approaches for data collection and ingest using Open Source tools such as Logstash and Kafka in a Hadoop ecosystem.
Advanced knowledge of data science concepts and applied knowledge of practices and methods.

If Verizon and this role sound like a fit for you, we encourage you to apply even if you don’t meet every “even better” qualification listed above.

Where you’ll be working

In this hybrid role, you’ll have a defined work location that includes work from home and a minimum eight assigned office days per month that will be set by your manager.