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Aws Machine Learning Jobs Near Me

The Hartford

AI Machine Learning Engineer

Columbus, OH · Hybrid

$100K - $151K/yr

The Hartford is seeking AI Machine Learning Engineer to build Machine Learning Operations (MLOps ... Development experience developing solutions within AWS, GCP or both. Exposure to developing ...

Bespoke Labs

Machine Learning Engineer

Columbus, OH · On-site

... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ... HPC (AWS, GCP, SLURM, or Ray) Solid understanding of evaluation methodology -- held-out sets ...

J.P. Morgan

WFP Machine Learning Scientist

Columbus, OH · On-site +1

Prior experience with public cloud technologies such as Amazon Web Services(AWS), Azure or Google ... machine learning. We have a strong partnership with Technology, which provides cutting edge data ...

The Hartford

Sr AI Machine Learning Engineer

Columbus, OH · Hybrid

$117K - $175K/yr

The Hartford is seeking Senior AI Machine Learning Engineer to build Machine Learning Operations ... Development experience developing solutions within AWS, GCP or both. * Experience developing ...

Whiz Global LLC

ML Engineer

Columbus, OH · On-site

Productionize machine learning models developed by Data Science teams. * Design and deploy Large Language Model (LLM) applications. * Integrate AI solutions into AWS and JPMorgan Chase internal cloud ...

New

The Hartford

Data Engineer

Columbus, OH

$110K - $132K/yr

The Hartford is developing industry-leading AI and machine learning capabilities to improve ... Familiarity with AWS and/or GCP cloud services. * Experience with source control systems such as ...

Huntington

Senior Data Scientist - AI

Columbus, OH · On-site +1

... Machine Learning technologies (e.g., AWS Sagemaker) Strong experience with machine learning environments (e.g., TensorFlow, scikit-learn, caret) Strong understanding of statistical methods and skills ...

Huntington

Senior Data Scientist - AI

Columbus, OH · On-site +1

Strong experience with Cloud Machine Learning technologies (e.g., AWS Sagemaker) * Strong experience with machine learning environments (e.g., TensorFlow, scikit-learn, caret) * Strong understanding ...

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As of Jul 25, 2026, the average hourly pay for aws machine learning in the United States is $70.06, according to ZipRecruiter salary data. Most workers in this role earn between $62.26 and $81.73 per hour, depending on experience, location, and employer.
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Aws Machine Learning jobs near you
A map of the United States highlighting the number of Aws Machine Learning job openings by state according to ZipRecruiter. The image is accompanied by a detailed chart listing the number of Aws Machine Learning job openings in each state, with California having the most at 2 and Hawaii the least at 0.
MLOps Engineer (Databricks/AWS)

MLOps Engineer (Databricks/AWS)

Inabia Solutions and Consulting, Inc.

Columbus, OH • On-site

Contractor

Posted 5 days ago

Job description

Overview
Inabia is seeking an MLOps Engineer (Databricks/AWS) to design, develop, and maintain end-to-end machine learning operations pipelines on Databricks running on AWS. This role demands deep hands-on expertise with Databricks Machine Learning, MLflow, and a broad suite of AWS services to build scalable, production-grade ML systems. The ideal candidate will partner closely with Data Scientists, Data Engineers, and Platform Engineering teams to productionize models and ensure robust, governed, and cost-efficient deployments.
Locations: Columbus, OH - Dallas, TX - Atlanta, GA (Onsite only)
Responsibilities
  • Design, develop, and maintain end-to-end MLOps pipelines on Databricks running on AWS.
  • Build and automate machine learning workflows covering data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
  • Deploy and manage ML models using MLflow Model Registry and Databricks Model Serving.
  • Develop and maintain CI/CD pipelines for ML solutions across development, staging, and production environments.
  • Collaborate with Data Scientists to productionize machine learning models and ensure reliable, repeatable deployments.
  • Monitor model health, prediction quality, data drift, and system performance, and implement automated retraining strategies where required.
  • Optimize Databricks workloads for performance, scalability, and cost efficiency.
  • Implement Infrastructure as Code using Terraform to provision and manage Databricks and AWS resources.
  • Ensure platform security, governance, and compliance using Unity Catalog and AWS IAM.
  • Troubleshoot production issues, perform root cause analysis, and continuously improve platform reliability.
  • Document MLOps processes, deployment standards, and operational best practices.

Key Qualifications
  • 10+ years of relevant experience in MLOps or a closely related data/ML engineering discipline.
  • Strong hands-on experience with Databricks Machine Learning.
  • Proficiency in Python, PySpark, and SQL for developing and operationalizing machine learning solutions.
  • Hands-on experience with MLflow for experiment tracking, model registry, model versioning, and lifecycle management.
  • Experience deploying and managing machine learning models using Databricks Model Serving and batch inference pipelines.
  • Strong understanding of the end-to-end ML lifecycle, including feature engineering, training, validation, deployment, monitoring, and retraining.
  • Experience building scalable ML pipelines using Databricks Workflows and Delta Lake.
  • Hands-on experience with AWS services including S3, IAM, EC2, Lambda, ECR, ECS/EKS, CloudWatch, and Secrets Manager.
  • Experience implementing CI/CD pipelines for Databricks and ML workloads using Git, Bitbucket, Jenkins, and Databricks Asset Bundles (DAB).
  • Experience with infrastructure automation using Terraform.
  • Strong understanding of Apache Spark architecture, optimization, and distributed data processing.
  • Experience working with Unity Catalog for governance, security, and access management.
  • Knowledge of model monitoring, data drift detection, and automated retraining strategies.
  • Understanding of MLOps best practices including reproducibility, versioning, testing, and governance.
  • Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications
  • Experience with multi-environment Databricks deployments spanning development, staging, and production.
  • Familiarity with cost optimization strategies for large-scale Databricks and AWS workloads.
  • Prior experience documenting MLOps operational standards for cross-functional teams.

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