Cloud Engineer-Machine Learning Ops

Title : Cloud EngineerMachineLearning Ops

Location : DurhamNC (Local Candidates only)

Duration : 12 months

Details : They are asking for more python and CI / CD experience aswell as everything below : This team wants LOCAL Durham NCcandidates only.

They have had a heck of a time with relos. We DONOT need a Data Scientist or ML Engineer for this. Team will notreview those profiles.

This is more of an AWS Cloud Engineer withsome ML Operations in the background. This person is going to bedeploying ML Models via AWS Sagemaker.

Will NOT be any new dev fromscratch.

MUST HAVE : AWS (Sagemaker Lambdas S3Buckets) / ETL / Python / Machine Learning Ops experience.Sagemaker is an absolute must plus CI / CD experience.

Description : Sr. AWS CloudEngineer w / Machine Learning Ops

As a CloudEngineer build and maintain large scale ML Infrastructure and MLpipelines. Contribute to building advanced analytics machinelearning platform and tools to enable both prediction andoptimization of models.

Extend existing ML Platform and frameworksfor scaling model training & deployment. Partner closely withvarious business & engineering teams to drive the adoptionintegration of model outputs.

This role is a critical element tousing the power of Data Science in delivering Fidelitys promise ofcreating the best customer experiences in financialservices.

Experience :

TheExpertise You Have

• Has Bachelors or MastersDegree in a technology related field (e.g. Engineering ComputerScience etc.).
• Experience in Object OrientedProgramming (Java Scala Python) SQL Unix scripting or relatedprogramming languages and exposure to some of Pythons ML ecosystem(numpy panda sklearn tensorflow etc.).
• Experience in building cloud native applications usingAWS services like S3 RDS CFT SNS SQS Step functions Event Bridgecloud watch etc.
• Experience with building datapipelines in getting the data required to build deploy and evaluateML models using tools like Apache Spark AWS Glue or otherdistributed data processing frameworks.
• Datamovement technologies (ETL / ELT) Messaging / Streaming Technologies(AWS SQS Kinesis / Kafka) Relational and NoSQL databases (DynamoDBEKS Graph database) API and inmemory technologies.
• Strong knowledge of developing highly scalabledistributed systems using Opensource technologies.
• 5 years of proven experience in implementing Big datasolutions in data analytics space.
• Experiencein developing ML infrastructure and MLOps in the Cloud using AWSSagemaker.
• Extensive experience working withmachine learning models with respect to deployment inference tuningand measurement required.
• Experience withCI / CD tools (e.g. Jenkins or equivalent) version control (Git)orchestration / DAGs tools (AWS Step Functions Airflow Luigi Kubeflowor equivalent).
• Solid experience in Agilemethodologies (Kanban and SCRUM).

The SkillsYou Bring

• You have strong technical design andanalysis skills.
• You the ability to deal withambiguity and work in fast paced environment.
• Your experience supporting criticalapplications.
• You are familiar with applieddata science methods feature engineering and machine learningalgorithms.
• Your Data wrangling experiencewith structured semistructure and unstructured data.
• Your experience building ML infrastructure with an eyetowards software engineering.
• You haveexcellent communication skills both through written and verbalchannels.
• You have excellent collaborationskills to work with multiple teams in the organization.
• Your ability to understand and adapt to changing businesspriorities and technology advancements in Big data and Data Scienceecosystem.
27 days ago