Machine Learning Solutions Architect

Posted about 8 hours agoViewed

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💎 Seniority level: Senior, 6+ years

📍 Location: United States, Latin America, India

🔍 Industry: Software Development

🗣️ Languages: English

⏳ Experience: 6+ years

🪄 Skills: AWSDockerPythonSoftware DevelopmentSQLCloud ComputingData AnalysisETLHadoopJavaKerasKubernetesMachine LearningMLFlowSnowflakeSoftware ArchitectureAlgorithmsAPI testingData engineeringData scienceREST APISparkTensorflowCommunication SkillsAnalytical SkillsCI/CDLinuxDevOpsPresentation skillsExcellent communication skillsScalaData modelingDebugging

Requirements:

At least 6 years experience as a Machine Learning Engineer, Software Engineer, or Data Engineer
4-year Bachelor's degree in Computer Science or a related field
Experience deploying machine learning 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
Hands-on experience in one or more big data ecosystem products/languages such as Spark, Snowflake, Databricks, etc.
Familiarity with multiple data sources (e.g. JMS, Kafka, RDBMS, DWH, MySQL, Oracle, SAP)
Systems-level knowledge in network/cloud architecture, operating systems (e.g., Linux), and storage systems (e.g., AWS, Databricks, Cloudera)
Production experience in core data technologies (e.g. Spark, HDFS, Snowflake, Databricks, Redshift, & Amazon EMR)
Development of APIs and web server applications (e.g. Flask, Django, Spring)
Complete software development lifecycle experience, including design, documentation, implementation, testing, and deployment
Excellent communication and presentation skills; previous experience working with internal or external customers

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
Demonstrate the business value of data by working with data scientists to manipulate and transform data into actionable insights
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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