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Aws Machine Learning Engineer Jobs (NOW HIRING)

CCT

Machine Learning Engineer (AWS)

Tulsa, OK · On-site

Requirements * 3+ years of experience in machine learning engineering, MLOps, or a closely related discipline. * Hands-on experience with AWS ML and data services - SageMaker (training, endpoints ...

Shipwell

Machine Learning Engineer

Austin, TX · On-site +1

About the Role As a Machine Learning Engineer at Shipwell, you'll play a pivotal role in building ... You'll optimize our AWS infrastructure, establish data integrity standards, and create scalable ...

CCT

Machine Learning Engineer (AWS)

Tulsa, OK · On-site +1

Requirements * 3+ years of experience in machine learning engineering, MLOps, or a closely related discipline. * Hands-on experience with AWS ML and data services - SageMaker (training, endpoints ...

Merican

AI / Machine Learning Engineer

PA · On-site

AI / Machine Learning Engineer (Contract) Location: Philadelphia, PA or Charlotte, NC Duration: 6 ... Design, develop, and deploy AI/LLM-powered applications in AWS production environments. * Build ...

CCT

Machine Learning Engineer (AWS)

Tulsa, OK · Remote

Requirements * 3+ years of experience in machine learning engineering, MLOps, or a closely related discipline. * Hands-on experience with AWS ML and data services -- SageMaker (training, endpoints ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

The Machine Learning Engineer will design and develop scalable training pipelines for multimodal AI ... AWS, GCP, Azure). • Excellent communication and collaboration skills, capable of working ...

Forhyre

Machine Learning Engineer

New York, NY

We are looking for a Machine Learning Engineer to help us create artificial intelligence products. Machine Learning Engineer responsibilities include creating machine learning models and retraining ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

They are seeking a Machine Learning Engineer to design and develop scalable training pipelines for ... AWS, GCP, Azure). • Excellent communication and collaboration skills, capable of working ...

DEKA

Machine Learning Engineer

Manchester, NH · On-site

We're looking for a Machine Learning Engineer who can operate at the intersection of backend ... Familiarity with AWS or equivalent cloud platforms * Strong problem-solving skills * Intrinsic ...

InstantServe LLC

Machine Learning Engineer

Machine Learning Engineer Location: Long Island City, NY 11101 (Onsite 4 Days/week) Type: Permanent ... Cloud Platforms (GCP, AWS, Azure) * Data Warehouses (BigQuery, Snowflake, Redshift) * LLMs / AI ...

Ksb

Machine Learning Engineer

Grovetown, GA

Machine Learning Engineer KSB GIW, Inc. Department: Engineering, Research & Development Reports to ... Cloud compute (AWS or Azure) and GPU-based training * Coursework or research projects in numerical ...

New

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ... Experience with cloud platforms such as Google Cloud Platform (preferred), AWS, or Azure, including ...

Capital One

Lead Machine Learning Engineer

San Francisco, CA · On-site +1

$120K - $159K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

Capital One

Lead Machine Learning Engineer

San Jose, CA · On-site +1

$120K - $158K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

New

Capital One

Lead Machine Learning Engineer

New York, NY · On-site +1

$112K - $147K/yr

Lead Machine Learning Engineer As a Capital One Machine Learning Engineer (MLE), you'll be part of ... Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google ...

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All JobsAws Machine Learning Engineer Jobs

Aws Machine Learning Engineer information

See salary details

$31.5K

$128.8K

$193.5K

How much do aws machine learning engineer jobs pay per year?

As of Jul 11, 2026, the average yearly pay for aws machine learning engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AWS Machine Learning Engineer, and why are they important?

To thrive as an AWS Machine Learning Engineer, you need strong proficiency in machine learning algorithms, programming languages like Python, and a solid understanding of cloud architecture, typically supported by a degree in computer science or a related field. Familiarity with AWS services such as SageMaker, Lambda, and S3, as well as relevant certifications like AWS Certified Machine Learning – Specialty, is highly valuable. Strong problem-solving, collaboration, and communication skills set top performers apart in this role. These skills ensure successful design, deployment, and optimization of scalable machine learning solutions on AWS that meet business needs.

What are AWS Machine Learning Engineers?

AWS Machine Learning Engineers are specialized professionals who design, build, deploy, and manage machine learning models using Amazon Web Services (AWS) cloud infrastructure. They leverage AWS tools and services, such as SageMaker, to create scalable and efficient machine learning solutions for businesses. Their responsibilities include data preparation, model training, optimization, deployment, and monitoring in a cloud environment. AWS Machine Learning Engineers often collaborate with data scientists, software engineers, and DevOps teams to integrate machine learning models into production systems.

How does an AWS Machine Learning Engineer typically collaborate with data scientists and DevOps teams?

As an AWS Machine Learning Engineer, you’ll work closely with data scientists to operationalize models, ensuring they are scalable and production-ready on AWS platforms. You’ll also frequently collaborate with DevOps teams to automate deployment pipelines, monitor model performance, and manage infrastructure using AWS services like SageMaker, Lambda, and CloudFormation. This cross-functional teamwork is essential for maintaining reliable, efficient ML workflows and for quickly resolving issues that arise in live environments.

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

AspectAws Machine Learning EngineerData Scientist
CredentialsAWS certifications, machine learning coursesStatistics, data analysis, programming skills
Work EnvironmentCloud platforms, AWS services, deployment pipelinesData analysis, modeling, research environments
Industry UsageTech, finance, healthcare using AWS for ML solutionsResearch, analytics, business intelligence
Search/Comparison IntentFocus on cloud-based ML deployment and engineeringFocus on data analysis and modeling

While both roles involve working with data and machine learning, Aws Machine Learning Engineers specialize in deploying ML models on AWS cloud platforms, focusing on infrastructure and scalable solutions. Data Scientists primarily analyze data, build models, and generate insights, often using a variety of tools and programming languages. The roles overlap in skills but differ in their primary focus and work environment.

More about Aws Machine Learning Engineer jobs
What states have the most Aws Machine Learning Engineer jobs? States with the most job openings for Aws Machine Learning Engineer jobs include:
What job categories do people searching Aws Machine Learning Engineer jobs look for? The top searched job categories for Aws Machine Learning Engineer jobs are:
Aws Machine Learning Engineer jobs near you
Infographic showing various Aws Machine Learning Engineer job openings in the United States as of July 2026, with employment types broken down into 94% Full Time, 2% Part Time, and 4% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.
Machine Learning Engineer (AWS)

Machine Learning Engineer (AWS)

CCT

Tulsa, OK • On-site

Other

Re-posted 10 days ago

Job description

Summary
 
We're looking for a Machine Learning Engineer to design, deploy, and operate production ML systems on Amazon Web Services. You'll own the full lifecycle in a real-world, high-stakes environment - from training and packaging through deployment, monitoring, retraining, security, and cost control.
 
This role sits at the intersection of ML engineering and MLOps and is core to CCT's analytics strategy. You'll partner closely with data scientists, engineers, and product stakeholders to turn complex time-series and transactional data into reliable, observable, and cost-effective ML services that our customers can trust.
 
You'll thrive here if you naturally dig into why models behave the way they do, enjoy tracing issues to their root cause, and like collaborating across disciplines to ship robust systems that are built to last.
What You'll Do
  • Build and maintain reproducible model training workflows on AWS (SageMaker, S3, Glue, etc.), making retraining, rollback, and experimentation routine rather than heroic.
  • Deploy and operate real-time and batch inference services with full CI/CD pipelines, versioning, and safe rollout strategies (canary, shadow, A/B) so changes are deliberate and observable.
  • Instrument production models for performance, data drift, latency, and errors - and automate retraining triggers when models drift out of tolerance.
  • Maintain model lineage, auditability, and traceability to meet the compliance, governance, and reporting needs of the regulated gaming industry.
  • Enforce least-privilege IAM, encryption, and secure data access patterns across the entire ML platform.
  • Treat cost as a first-class engineering metric - right-size infrastructure, balance batch vs. real-time workloads, and continually reduce platform spend without sacrificing reliability.
  • Collaborate with engineers, data scientists, and product teams to translate business problems into ML solutions, communicate tradeoffs clearly, and iterate based on feedback.
  • Continuously explore new AWS services, ML frameworks, and deployment patterns to improve reliability, observability, and developer velocity on the ML platform.
Requirements
  • 3+ years of experience in machine learning engineering, MLOps, or a closely related discipline.
  • Hands-on experience with AWS ML and data services - SageMaker (training, endpoints, pipelines), S3, Lambda, Step Functions, CloudWatch, MWAA (Apache Airflow).
  • Experience working with time series data, including feature engineering, seasonality handling, and temporal train/test splits.
  • Strong Python skills and familiarity with common ML frameworks (scikit-learn, PyTorch, XGBoost, or equivalent).
  • Experience building and maintaining CI/CD pipelines for ML systems.
  • Demonstrated ability to monitor and debug production ML systems - latency, drift, errors, and data quality - and drive issues to root cause.
  • Comfort with SQL and working with structured data at scale.
  • Able to work collaboratively across teams, assume positive intent, and communicate clearly with both technical and non-technical stakeholders.
  • Track record of self-directed learning and technical growth in areas like AWS, ML frameworks, or deployment patterns.
Certified Banana Picker 
 
Nice to Have
  • Experience in a regulated industry (gaming, finance, healthcare) where auditability, explainability, and compliance are first-class concerns.
  • Familiarity with feature stores, model registries, or ML metadata tools (e.g., MLflow, SageMaker Model Registry).
  • Experience with infrastructure-as-code (Terraform, CDK, or CloudFormation).
  • Exposure to data drift detection libraries or custom drift monitoring implementations.
Success Looks Like
  • Production models run reliably with clear, measurable business impact for casino operators.
  • Failures are observable, recoverable, and explainable - with logs, metrics, and traces that tell the full story.
  • ML systems scale predictably with usage and data volume, without runaway cost.
  • The ML platform becomes a trusted, well-understood part of CCT's product ecosystem - for both internal teams and external customers.

About CCT 

CCT is the creator of Casino Insight, the award-winning platform trusted by more than 350 casinos worldwide to automate cage operations, revenue audits, and operational analysis. Since 2012, Casino Insight has helped casinos replace manual work with streamlined workflows, improving accuracy, compliance, and profitability. 

Headquartered in Tulsa, Oklahoma, CCT integrates seamlessly with leading casino management, hospitality, and financial systems-delivering measurable ROI and empowering teams to work smarter at every level. 

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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