Remote Google Cloud Machine Learning Engineer Jobs

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.

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