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Aws Machine Learning Jobs (NOW HIRING)

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Machine Learning Engineer

Atlanta, GA · On-site

W2 Candidates Only We are seeking a Machine Learning Engineer to develop, deploy, and optimize ... AWS SageMaker or Azure ML * MLflow * Docker and Kubernetes * MLOps * Experience with AI/ML ...

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AI/ML Cloud Consultant

Short Hills, NJ · On-site

$63.75 - $86.75/hr

As an AWS Machine Learning Consultant, you will implement technical solutions as part of a team for customer engagements. This role requires strong teamwork, communication, patience and organization ...

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Aws Machine Learning information

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How much do aws machine learning jobs pay per hour?

As of Sep 4, 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.

What is an AWS Machine Learning?

An AWS Machine Learning job involves designing, building, and deploying machine learning models using Amazon Web Services (AWS) cloud infrastructure. Professionals in this role work with services like Amazon SageMaker, AWS Lambda, and AWS Glue to develop AI-driven applications. They optimize models for scalability, integrate them into cloud-based systems, and ensure efficient data processing. Strong knowledge of machine learning algorithms, AWS architecture, and MLOps best practices is essential for success in this role.

What does an AWS Machine Learning do?

In an AWS Machine Learning position, you'll typically design, develop, and deploy machine learning models using AWS services like SageMaker, Glue, and Lambda. Daily tasks often include data preprocessing, building and training models, and optimizing performance for production environments. You'll collaborate closely with data engineers, software developers, and business analysts to translate business needs into technical solutions. The role may also involve monitoring deployed models, managing cloud resources, and staying updated on new AWS features to ensure efficient and scalable machine learning workflows.

What are the key skills and qualifications needed for an AWS Machine Learning?

To thrive as an AWS Machine Learning professional, you need a strong understanding of machine learning principles, proficiency in programming languages like Python, and experience with AWS cloud services such as SageMaker. AWS Certified Machine Learning certification and familiarity with data pipelines, EC2, and Lambda are commonly required. Strong problem-solving, communication, and teamwork skills help you translate business requirements into technical solutions and collaborate effectively with diverse stakeholders. These skills are essential to efficiently deploy and manage scalable machine learning models that deliver business value in cloud-based environments.

Does AWS use machine learning?

AWS offers a wide range of machine learning services and tools, such as Amazon SageMaker, which enable developers and data scientists to build, train, and deploy machine learning models. As a cloud provider, AWS integrates machine learning into its infrastructure to support various applications, making it a key platform for machine learning professionals. Knowledge of AWS services and machine learning concepts is valuable for roles like AWS Machine Learning specialists.

Is AWS Machine Learning a high paying job?

AWS Machine Learning roles are generally well-paid due to the specialized skills required, such as expertise in cloud computing, data science, and machine learning frameworks. Salaries vary based on experience, location, and certifications, but they tend to be higher than average for tech roles with similar responsibilities.
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Aws Machine Learning jobs near you
Infographic showing various Aws Machine Learning job openings in the United States as of August 2026, with employment types broken down into 66% Full Time, 7% Part Time, and 27% Contract. Highlights an 87% In-person, and 13% Remote job distribution, with an average salary of $145,725 per year, or $70.1 per hour.

AI Developer Natural Language Processing Machine Learning AWS

Accord Technologies Inc.

New York, NY • On-site

Contractor

Re-posted yesterday

Job description

AI Developer – Natural Language Processing,  Machine Learning & AWS 
Loction: New York, NY  (Need Onsite day 1, hybrid 3 days from office).
Duraiton: Long term
Position type: W2 contract

Job Description:

We are seeking a highly skilled and motivated AI Developer specializing in Natural Language Processing (NLP) and Large Language Models (LLMs) to join our dynamic team. The ideal candidate will have strong hands-on experience in implementing LLMs, managing machine learning pipelines, and deploying AI solutions on cloud and server environments. Experience in the financial sector, particularly in equities, will be considered an advantage. 

Responsibilities:

  • Develop, implement, and optimize NLP solutions utilizing LLMs tailored for financial data and equities
  • Manage and deploy Machine Computing Platforms (MCP) to support scalable AI workloads.
  • Collaborate with data scientists and engineers to integrate AI models into production environments.
  • Maintain and enhance AI infrastructure on AWS cloud services and Linux-based servers.
  • Apply traditional machine learning techniques on structured large datasets to complement NLP efforts.
  • Troubleshoot and resolve software/hardware issues related to AI systems and server environments.
  • Stay updated with the latest advancements in NLP, LLMs, and machine learning best practices.

Requirements:

  • Deep knowledge of Natural Language Processing, including expertise with Large Language Models (e.g., GPT, BERT, similar architectures).
  • Hands-on experience in implementing, fine-tuning, and deploying LLMs in production.
  • Proven experience managing and operating MCP or similar machine learning platforms.
  • Strong proficiency with AWS cloud services (EC2, S3, Lambda, etc.) and experience working with Linux server environments.
  • Knowledge of traditional machine learning models applied on structured big data is a plus.
  • Prior experience in equities or financial data analysis is highly preferred.
  • Programming proficiency in Python, and familiarity with relevant AI frameworks (e.g., TensorFlow, PyTorch, Hugging Face).
  • Strong problem-solving skills and ability to work in a fast-paced, collaborative environment. 
Preferred,
  • Hands-on experience deploying AI models in financial or equities contexts.
  • Familiarity with data pipeline development and big data processing tools.