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Aws Machine Learning Jobs in Oklahoma (NOW HIRING)

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials ...

CTIO AI Engineering Manager

Oklahoma City, OK ยท On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... like AWS, GCP, Azure - Contributing to open-source projects or AI/ML publication Travel ...

CTIO AI Engineering Manager

Tulsa, OK ยท On-site

$73K - $244K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... like AWS, GCP, Azure - Contributing to open-source projects or AI/ML publication Travel ...

AI Solutions Engineering Delivery Lead

Tulsa, OK ยท On-site

$93K - $123K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... cloud platforms such as AWS, Azure, or Google Cloud Platform, with cloud foundational ...

AI Solutions Engineering Delivery Lead

Oklahoma City, OK ยท On-site

$95K - $125K/yr

Those in data science and machine learning engineering at PwC will focus on leveraging advanced ... cloud platforms such as AWS, Azure, or Google Cloud Platform, with cloud foundational ...

Lead AI / ML Engineer

Oklahoma City, OK ยท On-site

$95K - $125K/yr

Developing and deploying machine learning models and AI solutions. At least 2 from the following ... Understanding and working knowledge of cloud services and platforms (e.g., AWS, Azure, Google Cloud ...

Developing and deploying machine learning models and AI solutions. At least 2 from the following ... Understanding and working knowledge of cloud services and platforms (e.g., AWS, Azure, Google Cloud ...

Showing results 21-40

Aws Machine Learning information

See Oklahoma salary details

$9

$64

$88

How much do aws machine learning jobs pay per hour?

As of Aug 10, 2026, the average hourly pay for aws machine learning in Oklahoma is $64.69, according to ZipRecruiter salary data. Most workers in this role earn between $57.50 and $75.48 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 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.

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 most commonly searched types of Aws Machine Learning jobs in Oklahoma? The most popular types of Aws Machine Learning jobs in Oklahoma are:
What are popular job titles related to Aws Machine Learning jobs in Oklahoma? For Aws Machine Learning jobs in Oklahoma, the most frequently searched job titles are:
Infographic showing various Aws Machine Learning job openings in Oklahoma as of July 2026, with employment types broken down into 1% As Needed, 75% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $134,553 per year, or $64.7 per hour.

Senior Software Engineer Applied AI

Advanced Monitored Caregiving Inc.

Oklahoma City, OK โ€ข Remote

$99K - $130K/yr

Full-time

Posted 26 days ago


Job description

Senior Software Engineer: Applied AI (Voice Agents & ML Systems)

AMC Health · Remote (US) · Full-time

The pitch

We build and operate production AI voice agents that hold real phone conversations in a regulated healthcare setting, plus the machine learning and LLM pipelines around them. This is one seat that spans four disciplines that rarely come together: real-time systems, LLM engineering, traditional machine learning, and serious cloud infrastructure, all in production, all with real consequences. If you are the kind of engineer who gets restless doing one thing, this role is the opposite problem.

What you'll work across

Real-time voice AI

  • Streaming, low-latency speech-to-speech systems built on modern LLMs
  • Telephony and real-time media (call control, live audio streaming)
  • Audio handling and the quirks of real human conversation (interruptions, timing, noise)
  • Concurrency on a latency-sensitive path, where p99 matters and a stall is something a caller hears

LLM engineering

  • Wrapping nondeterministic models in deterministic control so they behave reliably in production
  • Multi-model pipelines, prompt design, and cost/latency budgeting
  • Evaluation harnesses, including LLM-as-judge and automated agent-tests-agent approaches
  • Agentic tooling that gives AI systems safe, structured access to infrastructure

Traditional (non-LLM) machine learning

  • End-to-end ML pipelines: feature engineering, model training, and scheduled inference
  • Imbalanced, messy real-world data; calibration and explainability for non-technical consumers
  • Turning research notebooks into reproducible, auditable production pipelines

Cloud and infrastructure

  • Infrastructure as code across multiple environments (we run on AWS)
  • Managed compute, data, streaming, and orchestration services
  • Security engineering in a regulated setting: encryption, least-privilege access, strict data-handling discipline
  • Observability and telemetry-driven debugging, tracing a production issue from a metric anomaly to root cause

Plus occasional full-stack work on internal tools, and an engineering workflow that leans heavily on AI coding assistants, with human accountability for every change.

What you'll actually do

  • Ship and debug code on a live, real-time voice pipeline where latency and correctness are user-facing
  • Design control systems around LLMs: guardrails, budgets, watchdogs, safe fallbacks
  • Build and operate LLM evaluation and batch-analysis pipelines
  • Own traditional ML workflows from data to scheduled production inference
  • Trace production issues from a metric anomaly to root cause, including building the evidence when the cause is a vendor

Must-haves

  • 7+ years building and operating production backend systems, with strong general-purpose programming skills (we work primarily in Python)
  • Experience running distributed systems in the cloud; comfortable debugging from telemetry to root cause
  • Hands-on production experience with LLMs or generative AI (any provider or framework), plus the judgment to know when not to use a model
  • Working fluency across the traditional machine learning lifecycle (you productionize; you do not need to publish)
  • Disciplined in a regulated environment: small, reviewable changes and careful handling of sensitive data

Nice-to-haves

  • Real-time media or telephony experience
  • Front-end / full-stack ability
  • ML pipeline experience, vector search, or embeddings
  • Fluency with AI coding assistants (our workflows assume them, with human accountability for every change)

How we work

Smallest correct change wins. Every behavior change is validated against the live system. Evidence over opinion in debugging. Code review is rigorous. Safety and privacy gate everything.

Work authorization (no exceptions)

This role is open only to US citizens and lawful permanent residents (Green Card holders). We cannot consider candidates who require visa sponsorship now or in the future, and we are unable to make exceptions of any kind.

How to apply

Please submit both of the following:

  • Your LinkedIn profile URL
  • A phone number where we can reach you

A resume is welcome but optional; the two items above are required.