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Machine Learning Engineer Opt Jobs in Rhode Island

Samprasoft

Machine Learning Engineer

Smithfield, RI

Sr. Machine Learning Engineer Duration: 12 -24 Months Location: Merrimack, NH/ Smithfield, RI/ Westlake, TX/ Durham, NC/ Covington, KY/ Jersey City, NJ/ Boston, MA Candidate should be local or ...

Indeed

Machine Learning Engineer III

Providence, RI

Comscore, Total Visits, March 2025) Day to Day As a Machine Learning Engineer III, you will be a team lead. You will own one of the team's major workstreams, help drive technical direction for the ...

Indeed

Senior Machine Learning Engineer

Providence, RI

$105K - $145K/yr

Comscore, Total Visits, March 2025) Day to Day As a Senior Machine Learning Engineer on our Sourcing team, you will work on developing and deploying ML and AI solutions in production. You'll be ...

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Machine Learning Engineer Opt information

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models into production environments. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, reliable systems that organizations can use to make predictions or automate tasks. Their responsibilities include data preprocessing, choosing appropriate algorithms, model training, and ensuring the model's performance in real-world applications. Machine Learning Engineers often collaborate with data scientists, data engineers, and product teams to deliver intelligent solutions.

What is the difference between Machine Learning Engineer Opt vs Data Scientist?

AspectMachine Learning Engineer OptData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; certifications in ML toolsBachelor's or Master's in CS, Statistics, or related fields; data analysis certifications
Work EnvironmentDevelops, tests, and deploys ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, AI startups, e-commerce, financeResearch institutions, tech firms, consulting, finance
Common Search & ComparisonOften compared for technical skills and deployment focusCompared for data analysis and business insights

Machine Learning Engineers Opt focus on deploying scalable ML models in production environments, while Data Scientists primarily analyze data and develop models for insights. Both roles require strong technical skills, but their core responsibilities differ in application and deployment.

Is a machine learning engineer still in demand?

Yes, machine learning engineers are in high demand due to the growing adoption of AI and data-driven solutions across industries. They are sought after for their skills in programming, data analysis, and familiarity with tools like Python, TensorFlow, and cloud platforms, making this a strong career choice for those with relevant expertise.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances because they develop and refine AI models, requiring specialized skills in programming, data analysis, and domain knowledge. Jobs that involve complex problem-solving, creativity, and emotional intelligence, such as healthcare professionals, educators, and skilled tradespeople, are also expected to persist despite AI automation. Continuous learning and adapting to new tools and technologies will be essential for job security across many fields.

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

To thrive as a Machine Learning Engineer, you need a solid background in mathematics, statistics, and programming (especially Python), typically supported by a degree in computer science, engineering, or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch), data processing tools, and cloud platforms, along with relevant certifications, is highly valuable. Strong problem-solving ability, collaboration, and effective communication are standout soft skills in this role. These skills and qualities ensure the successful development, deployment, and integration of machine learning solutions that drive business value.

What is a $900,000 AI job?

A $900,000 AI-related job typically refers to high-level roles such as senior machine learning engineers, AI research directors, or chief AI officers, often in large tech companies or specialized firms. These positions usually require advanced skills in machine learning, deep learning, and data science, along with extensive experience and leadership responsibilities.

What are some common challenges Machine Learning Engineers face when deploying models to production environments?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, handling data drift, and integrating models seamlessly with existing systems when deploying to production. Monitoring model performance in real time and retraining models as new data becomes available are also critical tasks. Collaboration with data engineers and DevOps teams is essential to address infrastructure and deployment hurdles while maintaining model accuracy and reliability.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-paying industries such as finance or tech, can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.
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Machine Learning Engineer Opt jobs near you
Machine Learning Engineer

Machine Learning Engineer

Samprasoft

Smithfield, RI

Other

Posted 8 days ago

Job description

Sr. Machine Learning Engineer

Duration: 12 -24 Months

Location: Merrimack, NH/ Smithfield, RI/ Westlake, TX/ Durham, NC/ Covington, KY/ Jersey City, NJ/ Boston, MA

Candidate should be local or willing to work onsite on any of the above locations for 5 days/ month

The Expertise And Skills You Bring
  • Bachelor’s or Master’s Degree in a technology related field (e.g. Engineering, Computer Science, etc.)
  • 8+ years of proven experience in implementing Big data solutions in data analytics space
  • 2+ years of 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 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 with building data pipelines in getting the data required to build and evaluate ML models, using tools like Apache Spark 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
  • 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)
  • Strong technical design and analysis skills
  • Ability to deal with ambiguity and work in fast paced environment
  • Experience supporting critical applications
  • Familiarity with applied data science methods, feature engineering and machine learning algorithms
  • Data wrangling experience with structured, semi-structured and unstructured data
  • Experience building ML infrastructure, with an eye towards software engineering
  • Excellent communication skills, both through written and verbal channels
  • Excellent collaboration skills to work with multiple teams in the organization
  • Ability to understand and adapt to changing business priorities and technology advancements in Big data and Data Science ecosystem

Required Skills : Basic Qualification : Additional Skills : Please do not send junior candidates, Need 10 plus years of experience.

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