Find Cloud Engineer (Machine Learning Ops) Contract Job in Boston, Massachusetts, United States | Snaprecruit

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Cloud Engineer (Machine Learning Ops)

  • ... Boston, Massachusetts, United States
  • ... Full time
  • ... Salary: 75 per hour
  • Posted on: Feb 05, 2024       Expires on: Mar 21, 2024

Cloud Engineer (Machine Learning Ops)   

JOB TITLE:

Cloud Engineer (Machine Learning Ops)

JOB TYPE:

Contract

JOB LOCATION:

Boston Massachusetts United States

JOB DESCRIPTION:

Job Role: Cloud Engineer (Machine Learning Ops) Location: Durham, NC / Boston MA / Westlake TX (Hybrid 5 days a month ) Duration: 12+ Months contract with possibilities of conversion Type: W2 Contract The minimum pay range is $65/hr on W2 all-inclusive

This is more of an AWS Cloud Engineer with some ML Operations in the background.
This person is going to be deploying ML Models via AWS Sagemaker.


MUST HAVE:

  • AWS (Sagemaker, Lambdas S3 Buckets)
  • ETL
  • Python or Java
  • Machine Learning Ops experience.
  • Sagemaker is an absolute must.

The Expertise You Have

  • Has Bachelor s or Master s Degree in a technology related field (e.
    g.
    Engineering, Computer Science, etc.
    ).
  • Experience in Object Oriented Programming (Java, Scala, Python), SQL, Unix scripting or related programming languages and exposure to some of Python s ML ecosystem (numpy, panda, sklearn, tensorflow, etc.
    ).
  • Experience in building cloud native applications using AWS services like S3, RDS, CFT, SNS, SQS, Step functions, Event Bridge, cloud watch etc.
    ,
  • Experience with building data pipelines in getting the data required to build, deploy and evaluate ML models, using tools like Apache Spark, AWS Glue or other distributed data processing frameworks.
  • Data movement technologies (ETL/ELT), Messaging/Streaming Technologies (AWS SQS, Kinesis/Kafka), Relational and NoSQL databases (DynamoDB, EKS, Graph database), API and in-memory technologies.
  • Strong knowledge of developing highly scalable distributed systems using Open-source technologies.
  • 5+ years of proven experience in implementing Big data solutions in data analytics space.
  • Experience in developing ML infrastructure and MLOps in the Cloud using AWS Sagemaker.
  • Extensive experience working with machine learning models with respect to deployment, inference, tuning, and measurement required.
  • Experience with CI/CD tools (e.
    g.
    , Jenkins or equivalent), version control (Git), orchestration/DAGs tools (AWS Step Functions, Airflow, Luigi, Kubeflow, or equivalent).
  • Solid experience in Agile methodologies (Kanban and SCRUM).

The Skills You Bring

  • You have strong technical design and analysis skills.
  • You the ability to deal with ambiguity and work in fast paced environment.
  • Your experience supporting critical applications.
  • You are familiar with applied data science methods, feature engineering and machine learning algorithms.
  • Your Data wrangling experience with structured, semi-structure and unstructured data.
  • Your experience building ML infrastructure, with an eye towards software engineering.
  • You have excellent communication skills, both through written and verbal channels.
  • You have excellent collaboration skills to work with multiple teams in the organization.
  • Your ability to understand and adapt to changing business priorities and technology advancements in Big data and Data Science ecosystem.

The Value You Deliver

  • Designing & developing a feature generation & store framework that promotes sharing of data/features among different ML models.
  • Partner with Data Scientists and to help use the foundational platform upon which models can be built and trained.
  • Operationalize ML Models at scale (e.
    g.
    Serve predictions on tens of millions of customers).
  • Build tools to help detect shifts in data/features used by ML models to help identify issues in advance of deteriorating prediction quality, monitoring the uncertainty of model outputs, automating prediction explanation for model diagnostics.
  • Exploring new technology trends and leveraging them to simplify our data and ML ecosystem.
  • Driving Innovation and implementing solutions with future thinking.
  • Guiding teams to improve development agility and productivity.
  • Resolving technical roadblocks and mitigating potential risks.
  • Delivering system automation by setting up continuous integration/continuous delivery pipelines.

Position Details

POSTED:

Feb 05, 2024

EMPLOYMENT:

Full-time

SALARY:

75 per year

SNAPRECRUIT ID:

S-1707482200-dd4f104f242861849037721603724bf3

LOCATION:

Massachusetts United States

CITY:

Boston

Job Origin:

jpick2

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Cloud Engineer (Machine Learning Ops)    Apply

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Job Role: Cloud Engineer (Machine Learning Ops) Location: Durham, NC / Boston MA / Westlake TX (Hybrid 5 days a month ) Duration: 12+ Months contract with possibilities of conversion Type: W2 Contract The minimum pay range is $65/hr on W2 all-inclusive

This is more of an AWS Cloud Engineer with some ML Operations in the background. This person is going to be deploying ML Models via AWS Sagemaker.

MUST HAVE:

  • AWS (Sagemaker, Lambdas S3 Buckets)
  • ETL
  • Python or Java
  • Machine Learning Ops experience.
  • Sagemaker is an absolute must.

The Expertise You Have

  • Has Bachelor s or Master s Degree in a technology related field (e.g. Engineering, Computer Science, etc.).
  • Experience in Object Oriented Programming (Java, Scala, Python), SQL, Unix scripting or related programming languages and exposure to some of Python s ML ecosystem (numpy, panda, sklearn, tensorflow, etc.).
  • Experience in building cloud native applications using AWS services like S3, RDS, CFT, SNS, SQS, Step functions, Event Bridge, cloud watch etc.,
  • Experience with building data pipelines in getting the data required to build, deploy and evaluate ML models, using tools like Apache Spark, AWS Glue or other distributed data processing frameworks.
  • Data movement technologies (ETL/ELT), Messaging/Streaming Technologies (AWS SQS, Kinesis/Kafka), Relational and NoSQL databases (DynamoDB, EKS, Graph database), API and in-memory technologies.
  • Strong knowledge of developing highly scalable distributed systems using Open-source technologies.
  • 5+ years of proven experience in implementing Big data solutions in data analytics space.
  • Experience in developing ML infrastructure and MLOps in the Cloud using AWS Sagemaker.
  • Extensive experience working with machine learning models with respect to deployment, inference, tuning, and measurement required.
  • Experience with CI/CD tools (e.g., Jenkins or equivalent), version control (Git), orchestration/DAGs tools (AWS Step Functions, Airflow, Luigi, Kubeflow, or equivalent).
  • Solid experience in Agile methodologies (Kanban and SCRUM).

The Skills You Bring

  • You have strong technical design and analysis skills.
  • You the ability to deal with ambiguity and work in fast paced environment.
  • Your experience supporting critical applications.
  • You are familiar with applied data science methods, feature engineering and machine learning algorithms.
  • Your Data wrangling experience with structured, semi-structure and unstructured data.
  • Your experience building ML infrastructure, with an eye towards software engineering.
  • You have excellent communication skills, both through written and verbal channels.
  • You have excellent collaboration skills to work with multiple teams in the organization.
  • Your ability to understand and adapt to changing business priorities and technology advancements in Big data and Data Science ecosystem.

The Value You Deliver

  • Designing & developing a feature generation & store framework that promotes sharing of data/features among different ML models.
  • Partner with Data Scientists and to help use the foundational platform upon which models can be built and trained.
  • Operationalize ML Models at scale (e.g. Serve predictions on tens of millions of customers).
  • Build tools to help detect shifts in data/features used by ML models to help identify issues in advance of deteriorating prediction quality, monitoring the uncertainty of model outputs, automating prediction explanation for model diagnostics.
  • Exploring new technology trends and leveraging them to simplify our data and ML ecosystem.
  • Driving Innovation and implementing solutions with future thinking.
  • Guiding teams to improve development agility and productivity.
  • Resolving technical roadblocks and mitigating potential risks.
  • Delivering system automation by setting up continuous integration/continuous delivery pipelines.

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