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Machine Learning Engineer Jobs in Fort Mill, SC (NOW HIRING)

1 point system

Machine Learning Engineer

Fort Mill, SC · Remote

$48/hr

Job Title Machine Learning Engineer Location Remote Rate $48/hr on W2 Must Haves: Neaural networks NLP Python AZURE Pytorch or tensorflow Machine Learning Engineer / AI Engineer Role Role Overview ...

New

Bespoke Labs

Machine Learning Engineer

Charlotte, NC · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Bespoke Labs

Machine Learning Engineer

Rock Hill, SC · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Tihinsurance

Machine Learning Engineer

Charlotte, NC

... the machine learning function at a market-leading insurance company. As one of the first data ... Leverage continuous engineering practices to deliver business value regarding effectiveness of the ...

New

The Hartford

Sr AI Machine Learning Engineer

Charlotte, NC · Hybrid

$117K - $175K/yr

The Hartford is seeking Senior AI Machine Learning Engineer to build Machine Learning Operations (MLOps) services for the Global Specialty Applied AI team. The Hartford is developing industryleading ...

Euclid Innovations

ML Engineer

Charlotte, NC · On-site

Euclid Innovations is seeking a skilled and experienced Machine Learning Engineer to design and implement solutions for extracting, processing, and storing information from large-scale document ...

Varsity Tutors

Machine Learning Tutor

Charlotte, NC · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Matthews, NC · Remote

$18 - $40/hr

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

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All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Fort Mill, SC

Machine Learning Engineer information

See Fort Mill, SC salary details

$27.7K

$113.2K

$170K

How much do machine learning engineer jobs pay per year?

As of Jul 29, 2026, the average yearly pay for machine learning engineer in Fort Mill, SC is $113,156.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,200.00 and $136,200.00 per year, depending on experience, location, and employer.

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-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

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 strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

What are some common challenges faced by Machine Learning Engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What are the most commonly searched types of Machine Learning Engineer jobs in Fort Mill, SC? The most popular types of Machine Learning Engineer jobs in Fort Mill, SC are:
What are popular job titles related to Machine Learning Engineer jobs in Fort Mill, SC? For Machine Learning Engineer jobs in Fort Mill, SC, the most frequently searched job titles are:
What cities near Fort Mill, SC are hiring for Machine Learning Engineer jobs? Cities near Fort Mill, SC with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Fort Mill, SC as of July 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $113,156 per year, or $54.4 per hour.
Machine Learning Engineer

Machine Learning Engineer

1 point system

Fort Mill, SC • Remote

$48/hr

Contractor

Posted yesterday

Job description

Hi ,
I hope you're doing well.

I'm reaching out regarding an exciting opportunity that I believe aligns well with your background and skill set.

To move forward, could you please provide the following details along with latest copy of resume:

Work Authorization and Expiry (If any)

LinkedIn Profile URL

Current Location with Zip code

Pay Expectation on W2 (hourly)

Complete JD:

Job Title

Machine Learning Engineer

Location

Remote

Rate

$48/hr on W2

Must Haves:
Neaural networks
NLP
Python
AZURE
Pytorch or tensorflow
Job Description:
Machine Learning Engineer / AI Engineer Role

Role Overview

This role is focused on developing, deploying, and optimizing machine learning models for enterprise applications. The ideal candidate should have strong hands-on experience with machine learning algorithms, neural networks, NLP, Python/R/SQL, modern ML frameworks, Microsoft Azure, and DevOps/MLOps practices. This is not just a data science research role — the candidate needs to be able to build models and support deployment/management in a production environment.

Must-Have Skills

The candidate must have hands-on experience with:

  • Supervised and/or unsupervised machine learning algorithms
  • Neural networks
  • Natural Language Processing, NLP
  • Python
  • R
  • SQL
  • TensorFlow, Keras, and/or PyTorch
  • Microsoft Azure cloud platform
  • DevOps and/or MLOps practices
  • Model development, deployment, optimization, and lifecycle management

Strong Fit Profile

A strong candidate will have experience building and deploying machine learning models from end to end. They should be comfortable selecting the right algorithms, preparing and analyzing data, training models, evaluating performance, and deploying models into cloud-based environments.

They should also understand MLOps concepts such as CI/CD for ML models, version control, monitoring, automation, model retraining, and production support. Azure experience is important, especially if they have used Azure Machine Learning, Azure DevOps, Azure Databricks, Azure Functions, or related cloud services.

Key Screening Questions

Machine Learning Experience

  1. Can you walk me through a machine learning model you developed from start to finish?
  2. What supervised learning algorithms have you worked with most often?
  3. What unsupervised learning algorithms have you used, and what business problems were they solving?
  4. How do you determine which algorithm is the best fit for a use case?
  5. How do you evaluate model performance and accuracy?

Neural Networks / NLP

  1. What experience do you have building or working with neural networks?
  2. Have you worked on any NLP-related projects? If so, what was the use case?
  3. What NLP techniques, libraries, or models have you used?
  4. Have you worked with text classification, sentiment analysis, entity extraction, chatbots, or language models?
  5. How do you clean and prepare text data for NLP models?

Tools / Programming Languages

  1. How strong would you rate your Python skills?
  2. Have you used R in a professional setting? If yes, for what type of work?
  3. How have you used SQL in your machine learning or data science work?
  4. Which ML frameworks have you used: TensorFlow, Keras, PyTorch?
  5. Which framework are you strongest in, and why?

Azure / Cloud Experience

  1. What Microsoft Azure services have you used for machine learning or data work?
  2. Have you used Azure Machine Learning before?
  3. Have you deployed ML models into Azure environments?
  4. Have you worked with Azure DevOps, Azure Databricks, Azure Functions, or Azure Pipelines?
  5. Can you describe a cloud-based ML project you supported?

DevOps / MLOps

  1. What does MLOps mean in your previous experience?
  2. Have you built or supported CI/CD pipelines for machine learning models?
  3. How have you handled model versioning, monitoring, or retraining?
  4. Have you worked with containerization tools like Docker or Kubernetes?
  5. How do you manage models once they are in production?

Deployment / Optimization

  1. Have you deployed machine learning models into production?
  2. What challenges have you faced during model deployment?
  3. How do you monitor model performance after deployment?
  4. Have you optimized models for performance, scalability, or accuracy?
  5. What steps do you take when a model’s performance starts to decline?

Candidate Must Be Able to Explain

The recruiter should listen for examples where the candidate can clearly explain:

  • What business problem they were solving
  • What data they used
  • What algorithm or model they selected
  • Why they selected that approach
  • What tools/frameworks they used
  • How they measured success
  • How the model was deployed
  • How the model was monitored or maintained
  • Their exact role in the project

Green Flags

Strong candidates may mention experience with:

  • Azure Machine Learning
  • Azure DevOps
  • Azure Databricks
  • CI/CD pipelines
  • Model monitoring
  • Model retraining
  • Model versioning
  • Feature engineering
  • NLP pipelines
  • Text classification
  • Neural network architecture
  • TensorFlow, Keras, or PyTorch in production
  • Python-heavy ML development
  • SQL for data extraction and analysis
  • End-to-end model deployment
  • Production ML environments
  • MLOps lifecycle ownership

Red Flags

Watch out for candidates who:

  • Only have academic or theoretical ML experience
  • Cannot explain specific models they have built
  • Have used Python only for scripting, not ML development
  • Have no Azure experience
  • Have no production deployment experience
  • Only know ML frameworks at a high level
  • Have no DevOps or MLOps exposure
  • Cannot explain supervised vs. unsupervised learning
  • Have only used pre-built tools without understanding the models
  • Cannot describe how they monitored or optimized a model after deployment

Quick Recruiter Intake Notes

Top priority: ML model development + deployment

Cloud requirement: Microsoft Azure

Programming must-haves: Python, R, SQL

Frameworks: TensorFlow, Keras, PyTorch

AI/ML focus: Supervised learning, unsupervised learning, neural networks, NLP

Operational focus: DevOps/MLOps, model deployment, monitoring, optimization

Best candidates: Hands-on ML engineers or data scientists with production deployment experience

Avoid: Candidates who only have academic ML exposure or no Azure/MLOps experience

Thank You

Ranjeet Kumar | 1Point System LLC

Senior Technical Recruiter
• Email: ranjeet@1pointsys.com • Fax: 803-832-7973 • www.1pointsys.com

https://www.linkedin.com/in/ranjeet-kumar-829a4525b/

If you are unable to reach me directly, please feel free to contact my supervisor at ashish.trivedi@1pointsys.com . They will be able to assist you with any inquiries or provide the support you need.


115 Stone Village Drive • Suite C • Fort Mill, SC • 29708

         An E-Verified company | An Equal Opportunity Employer