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Remote Aws Machine Learning Jobs in Utah (NOW HIRING)

Bread Financial

Lead Data Scientist

Draper, UT · On-site +1

$138K - $272K/yr

Advanced machine learning modeling and/or technical expertise in developing market differentiation ... Normal office environment. (Remote or Hybrid), 3 to 4 days per month are required in office if ...

asburyauto

IT Enterprise Cloud Architect

Draper, UT · On-site +1

... eligible for remote or hybrid work. The IT Enterprise Cloud Architect to lead the design ... Design and evolve enterprise AWS cloud architecture * Establish governance and best practices

asburyauto

IT Enterprise Cloud Architect

Draper, UT · On-site +1

... eligible for remote or hybrid work. The IT Enterprise Cloud Architect to lead the design ... Design and evolve enterprise AWS cloud architecture * Establish governance and best practices

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All JobsAws Machine Learning JobsRemote Aws Machine Learning JobsRemote Aws Machine Learning Jobs in Utah

Remote Aws Machine Learning information

What are the key skills and qualifications needed to thrive as a Remote AWS Machine Learning Engineer, and why are they important?

To thrive as a Remote AWS Machine Learning Engineer, you need a strong background in machine learning algorithms, statistical analysis, and proficiency in programming languages such as Python, often supported by a relevant degree or certification. Familiarity with AWS services like SageMaker, Lambda, and EC2, as well as experience using cloud-based ML tools and AWS Certified Machine Learning credentials, is typically required. Excellent problem-solving skills, self-motivation, and clear written communication are valuable soft skills for remote collaboration and project management. These skills ensure effective model development, seamless deployment on cloud infrastructure, and successful remote teamwork in delivering scalable ML solutions.

What are remote AWS Machine Learning jobs?

Remote AWS Machine Learning jobs involve working with Amazon Web Services' suite of machine learning tools and services, such as SageMaker, to build, train, and deploy machine learning models. These positions allow professionals to work from anywhere, collaborating with teams virtually while leveraging AWS infrastructure to solve data-driven problems. Responsibilities often include data preprocessing, model development, and deploying scalable solutions in the cloud. Typical job titles may include Machine Learning Engineer, Data Scientist, or AI Developer, all with a focus on AWS technologies. These roles require strong programming skills, experience with cloud computing, and a background in machine learning or data science.

What is the difference between Remote Aws Machine Learning vs Remote Data Scientist?

AspectRemote Aws Machine LearningRemote Data Scientist
Required CredentialsAWS certifications, machine learning coursesStatistics, data analysis, programming skills
Work EnvironmentCloud platforms, AWS services, remote teamsData analysis, modeling, research in remote settings
Industry UsageTech, finance, healthcare using AWS ML toolsResearch, consulting, analytics across industries

Remote AWS Machine Learning specialists focus on deploying machine learning models using AWS cloud services, requiring AWS certifications and cloud expertise. Remote Data Scientists analyze data, build models, and interpret results, often with a stronger emphasis on statistics and programming. While both roles work remotely and involve data, AWS Machine Learning roles are more cloud and deployment-oriented, whereas Data Scientists focus on data analysis and research.

What are some common challenges faced by remote AWS Machine Learning engineers, and how can they be addressed?

Remote AWS Machine Learning engineers often face challenges related to communication and collaboration, especially when working across different time zones and with cross-functional teams. Ensuring secure access to data and cloud resources is another key concern, given the sensitive nature of many machine learning projects. To overcome these challenges, engineers should leverage AWS collaboration tools, maintain clear documentation, and participate in regular virtual meetings. Additionally, setting up robust security protocols and using AWS Identity and Access Management (IAM) helps safeguard project assets while enabling effective teamwork.
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Remote Aws Machine Learning jobs near you

Innovations and AI Solutions Engineer

Wsgr

Salt Lake City, UT • On-site, Remote

Full-time

Posted 28 days ago

Job description

Wilson Sonsini is the premier legal advisor to technology, life sciences, and other growth enterprises worldwide. We represent companies at every stage of development, from entrepreneurial start-ups to multibillion-dollar global corporations, as well as the venture firms, private equity firms, and investment banks that finance and advise them. The firm has approximately 1,100 attorneys in 17 offices: 13 in the U.S., two in China, and two in Europe. Our broad spectrum of practices and entrepreneurial spirit allow exceptional opportunities for professional achievement and career growth.

The Innovation and AI Solutions Engineer position is part of the firm's Innovation Department.This position will be responsible for developing, optimizing and growing the firm's corpus of innovation and AI solutions for both practice and enterprise side use cases, as well as optimizing data collection and flows across these solutions.The role will support our attorneys and staff with software development, low/no-code solutions, and data initiatives. The Innovation and AI Solutions Engineer is a self-directed, people-oriented employee who is comfortable supporting the development and data needs of the organization, including clients, attorneys, practice groups and administrative teams.

This position is available as a hybrid or remote work schedule.

Essential Duties, Responsibilities

  • Support innovation projects by facilitating and participating in discussions, meetings with management and department groups, developing business processes, implementing data integrations and recommending best practices on the effective use of data and data analytics tools.

  • Build and maintain innovation and AI solutions alongside optimal data API and pipeline architectures.

  • Design, build and implement machine learning models, including the development of AI Models and prompts for various applications.

  • Collaborate with business users and technical teams to support and improve how data is collected, analyzed and reported throughout the organization.

  • Analyze users needs to determine business and data requirements, effectively applying technology to meet the firm's strategic objectives.

  • Assemble and manage large, complex data sets that meet business and technical requirements.

  • Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, and improving infrastructure within assigned projects and data systems.

  • Build system infrastructure and processes for the efficient extraction, transformation, loading and integration of data to and from a wide variety of data sources using SQL, Power BI and Azure technologies.

  • Develop and/or apply analytics tools that utilize data pipelines to provide actionable insights into customer acquisition, operational efficiency, billing analytics, case outcomes, and other key business and legal practice performance metrics.

  • Ensure firm and client data security across multiple office locations, data centers, vendors and applications.

  • Develop data visualization and reporting tools to effectively convey meaningful insights from complex and diverse data sets.

  • Leverage statistics and computational techniques in problem solving.

  • Be familiar with common NLP and machine learning techniques.

  • Research and use applicable models in creative problem solving.

  • Work with GitHub and software version control.

  • Demonstrate the ability to efficiently and smartly document code and end-to-end processes.

  • Implement models in a production environment via custom API's and enterprise solutions. such as Azure.

Experience, Knowledge and Abilities

  • Extensive experience and working knowledge with SQL and relational databases, including query authoring, database management, and creating/maintaining large data stores in SQL and cloud platforms such as Azure or AWS.

  • Experience building and optimizing API's and data pipelines, architectures and data sets.

  • Strong analytic skills related to working with structured and unstructured datasets.

  • Ability to learn new tools and coding languages where required.

  • Ability to organize tasks and priorities under minimal supervision.

  • Experience performing root cause analysis on internal and external data, data integrations and processes to solve specific business problems and identify opportunities for improvement.

  • Experience developing processes that support data transformation, integrations, data structures, metadata, dependency and workflow management.

  • A successful history of manipulating, processing and extracting value from large, disconnected datasets.

  • Experience collaborating with cross-functional teams and stakeholders in a dynamic environment.

  • Ability to handle sensitive and confidential information responsibly.

  • Experience with AI/ML data preparation including feature engineering, data preprocessing, and dataset versioning for machine learning workflows.

  • Knowledge of data bias detection and mitigation techniques to ensure AI models are fair and representative across different legal contexts.

  • Experience with vector databases and embeddings for semantic search and retrieval-augmented generation (RAG) applications.

Technological Proficiency

  • Expertise in applying the following technologies:

    • SQL Server, T-SQL, SSIS & SSRS, Stored Procedures

    • DataWarehousing experience - ETL & ELT

    • PowerBI, Data Analysis Expressions (DAX)

    • Excel / PowerQuery

    • Programming and scripting languages: Python, R, C++, Julia, Javascript, SQL

    • API integration and development tools and scripting

    • Extensive experience working with a variety of data file formats, such as JSON, XML, SQL

  • Additional skills that would be highly advantageous include:

    • PowerShell

    • Regular Expression (Regex)

    • VBA, MS Access & Excel

    • Documentation & Process Mapping

    • Dynamic visualization tools, such as Microsoft Power BI, Tableau, Domo, etc.

    • Experience developing and applying machine learning models using Python, R, SQL and Azure Machine Learning

    • Experience integrating legal industry, line-of-business applications, such as SharePoint, Aderant Financial System, Salesforce.com/CRM, NetDocs/DMS

    • Large Language Model Integration: Experience with OpenAI API, Azure OpenAI, Anthropic Claude, or similar

    • Embedding Models: Familiarity with sentence transformers, OpenAI embeddings, or domain-specific legal embeddings

  • Law firm experience a plus.

Requirements

  • 4+ years of experience in a Data Engineer/DataOps-DevOps role.

  • Bachelor's degree and/or graduate degree in Computer Science, Data Science, Data Analytics, Information Systems or equivalent discipline.

  • Experience with Microsoft SQL Server and related Microsoft data management and integration technologies.

  • Excellent verbal and written communication and interpersonal skills

Preferred

  • AI/ML Data Preparation Certification or equivalent coursework in machine learning data engineering.

  • Experience with legal technology platforms and understanding of legal workflow requirements.

  • Knowledge of data privacy regulations (GDPR, CCPA, HIPAA) as they apply to AI systems in legal contexts.

The primary location for this job posting is in Palo Alto, but other locations may be listed. The actual base pay offered will depend upon a variety of factors, including but not limited to the selected candidate's qualifications, years of relevant experience, level of education, professional certifications and licenses, and work location. The anticipated pay range for this position is as follows:Palo Alto, New York, San Francisco: $116,875 - $158,125 per year. Austin, Boston, Boulder, Century City, Delaware, Los Angeles, Salt Lake City, San Diego, Seattle, Washington, D.C., and all other locations: $105,400 - $142,600 per year.

The compensation for this position may include a discretionary year-end merit bonus based on performance. We offer a highly competitive salary and benefits package.

Benefits information can be found here. Equal Opportunity Employer (EOE).

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