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Trainee Edge Ai Machine Learning Jobs in Virginia

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA ... Contribute to AI product development activities across the lifecycle, including prototyping ...

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Trainee Edge Ai Machine Learning information

What is the difference between Trainee Edge Ai Machine Learning vs Data Analyst?

AspectTrainee Edge Ai Machine LearningData Analyst
Required CredentialsBasic programming, introductory AI/ML coursesStatistics, data visualization, Excel, SQL
Work EnvironmentTech companies, AI startups, R&D labsBusiness, finance, marketing departments
Industry UsageDeveloping AI models, machine learning pipelinesInterpreting data, generating reports

While both roles involve working with data, Trainee Edge Ai Machine Learning focuses on developing and training AI models, requiring programming and machine learning knowledge. Data Analysts primarily interpret existing data to inform business decisions, emphasizing statistical analysis and visualization skills. The roles differ in technical depth and focus but often overlap in data handling and industry usage.

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Machine Learning Engineer - LLM / MLOps

HRC Global Services

Reston, VA โ€ข On-site

Full-time

Re-posted 22 days ago


Job description

Machine Learning Engineer – LLM / MLOps

Job Title: Machine Learning Engineer – LLM & MLOps
Location: Remote (U.S.)
Employment Type: Full-Time

About the Opportunity:
An exciting role for an ML Engineer to build scalable ML systems, deploy models, and work with cutting-edge AI technologies including LLMs and RAG architectures.

Key Responsibilities:

  • Build, train, and deploy ML models at scale
  • Develop reusable pipelines using Databricks and MLflow
  • Implement CI/CD workflows for ML deployment
  • Work with LLMs, RAG, and AI agent frameworks
  • Monitor model performance, drift, and retraining cycles

Required Skills:

  • 5+ years of ML Engineering experience
  • Strong Python programming and ML frameworks (PyTorch, TensorFlow, Scikit-learn)
  • Hands-on experience with Databricks, MLflow, PySpark
  • Experience with AWS (S3, SageMaker, Lambda, etc.)
  • Strong understanding of MLOps and model lifecycle

Preferred:

  • Experience building AI-driven applications (Streamlit, Gradio)
  • Strong system design and data pipeline experience
  • Business understanding of AI applications

Clearance: Public Trust (or eligible)

Hashtags:
#MLEngineer #MachineLearning #MLOps #LLM #AWS #Databricks #PySpark #AIEngineering #RemoteJobs #HiringNow