Senior Machine Learning Engineer

Posted 23 days agoViewed

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💎 Seniority level: Senior, 4+ years

📍 Location: United States, Latin America, India

🔍 Industry: Software Development

🏢 Company: phData👥 501-1000💰 $2,499,997 Seed about 7 years agoInformation Services Analytics Information Technology

🗣️ Languages: English

⏳ Experience: 4+ years

🪄 Skills: AWSDockerPythonSQLCloud ComputingDjangoETLFlaskGCPJavaKerasKubernetesMachine LearningSnowflakeAPI testingAzureData engineeringData scienceREST APISparkTensorflowCommunication SkillsAnalytical SkillsCI/CDProblem SolvingScalaData visualizationData modelingData analyticsData management

Requirements:

At least 4 years experience as a Machine Learning Engineer, Software Engineer, or Data Engineer
4-year Bachelor's degree in Computer Engineering or a related field
Experience deploying data science models in a production setting
Expertise in Python, Scala, Java, or another modern programming language
The ability to build and operate robust data pipelines using a variety of data sources, programming languages, and toolsets
Strong working knowledge of SQL and the ability to write, debug, and optimize distributed SQL queries
Experience working with Data Science/Machine Learning software and libraries such as h2o, TensorFlow, Keras, scikit-learn, etc.
Experience with Docker, Kubernetes, or some other containerization technology
Familiarity with multiple data source systems (e.g. JMS, Kafka, RDBMS, DWH, MySQL, Oracle, SAP)
Systems-level knowledge in network/cloud architecture, operating systems (e.g., Linux), storage systems (e.g., AWS, Databricks, Cloudera)
Production experience in core data technologies (e.g. Spark, Pandas)
Development of APIs and web server applications (e.g. Flask, Django, Spring)

Responsibilities:

Design and create environments for data scientists to build models and manipulate data
Work within customer systems to extract data and place it within an analytical environment
Learn and understand customer technology environments and systems
Define the deployment approach and infrastructure for models and be responsible for ensuring that businesses can use the models we develop
Reveal the true value of data by working with data scientists to manipulate and transform data into appropriate formats in order to deploy actionable machine learning models
Partner with data scientists to ensure solution deployability—at scale, in harmony with existing business systems and pipelines, and such that the solution can be maintained throughout its life cycle
Create operational testing strategies, validate and test the model in QA, and implementation, testing, and deployment
Ensure the quality of the delivered product

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