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Ml Infrastructure Engineer Jobs in Georgia (NOW HIRING)

Daimler

AI/ML Platform Engineer

Atlanta, GA · On-site

This role designs, builds, and operates shared AI/ML infrastructure, deployment pipelines, model lifecycle tooling, and reusable engineering services that enable data scientists, AI engineers, and ...

Deloitte

Azure/Databricks Infrastructure Engineer

Atlanta, GA · On-site

$53.50 - $71.75/hr

Preferred: • Experience supporting enterprise analytics, ETL, Snowflake, or modern data engineering ecosystems • Experience supporting AI/ML or advanced analytics infrastructure environments • ...

Flight Centre Travel Group (usa) Inc

$92K - $125K/yr

Senior Cloud Infrastructure Engineer - South Bank, QLD Apply now Refer a friend Job no: 531618SS ... ML or automation workloads advantageous * Azure certifications desirable (AZ-104, AZ-400, AZ-500 ...

AppFolio

GA · On-site

$16.25 - $19.25/hr

You understand how ML infrastructure decisions compound over time. * Production builder: You've ... engineering. * Reliability mindset: You treat ML infra like any other production system: SLOs ...

Bricklayer AI

Platform Engineering Leader

Atlanta, GA · On-site

... ROI on infrastructure investments • Implement comprehensive monitoring, alerting, and SRE ... ML infrastructure, including GPU workloads, vector databases, and LLM serving platforms • ...

AGS

DevOps Platform Engineer

Duluth, GA · On-site

$48.50 - $66.50/hr

AI/ML infrastructure experience - has deployed LLM-based applications or ML models to production ... Engineer builds and maintains the technical infrastructure that enables AGS's entire AI program to ...

Worldpay

ML Software Engineering Lead

Atlanta, GA

$98K - $129K/yr

Partner closely with research-focused data science teams, business stakeholders, infrastructure ... ML software engineering, ML ops, ML engineering, or ML research experience. * 5+ years of ...

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All JobsMl Infrastructure Engineer JobsMl Infrastructure Engineer Jobs in Georgia

Ml Infrastructure Engineer information

See Georgia salary details

$39.3K

$107.3K

$153.7K

How much do ml infrastructure engineer jobs pay per year?

As of Aug 6, 2026, the average yearly pay for ml infrastructure engineer in Georgia is $107,292.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,800.00 and $119,100.00 per year, depending on experience, location, and employer.

What is the difference between Ml Infrastructure Engineer vs Data Engineer?

AspectML Infrastructure EngineerData Engineer
Required CredentialsBachelor's/Master's in CS, experience with cloud platforms, scripting, and ML toolsBachelor's/Master's in CS, experience with databases, ETL, and data pipelines
Work EnvironmentFocus on deploying and maintaining ML systems, cloud infrastructure, and automationDesigning and building data pipelines, managing large datasets, and data storage
Employer & Industry UsageTech companies, AI startups, research labsFinance, healthcare, e-commerce, and data-driven industries

The ML Infrastructure Engineer specializes in building and maintaining the infrastructure that supports machine learning models, focusing on deployment, scalability, and automation. In contrast, Data Engineers primarily develop data pipelines and manage large datasets to enable data analysis and business intelligence. Both roles require strong technical skills and often overlap, but their core focus areas differ significantly.

What are popular job titles related to Ml Infrastructure Engineer jobs in Georgia? For Ml Infrastructure Engineer jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Ml Infrastructure Engineer jobs in Georgia look for? The top searched job categories for Ml Infrastructure Engineer jobs in Georgia are:
What cities in Georgia are hiring for Ml Infrastructure Engineer jobs? Cities in Georgia with the most Ml Infrastructure Engineer job openings:
Ml Infrastructure Engineer jobs near you
Infographic showing various Ml Infrastructure Engineer job openings in Georgia as of August 2026, with employment types broken down into 94% Full Time, 2% Part Time, and 4% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $107,292 per year, or $51.6 per hour.

AI/ML Ops & Infrastructure Engineer - Q126

R2 Technologies Corporation

Alpharetta, GA • On-site

$105K - $137K/yr

Full-time

Re-posted 21 hours ago

Job description

Overview:
Job Title: AI/ML Ops & Infrastructure Engineer
Company: R2 Technologies
Location: Alpharetta, GA (Hybrid / Remote Options Available)
Employment Type: Full-Time / Contractual
About R2 Technologies: R2 Technologies is a Certified Minority Business Enterprise (MBE) headquartered in Alpharetta, GA. With over two decades of experience across global markets, we have built a reputation as a trusted partner for IT staffing excellence and cutting-edge digital product innovation. We are driven by innovation and operate on a simple philosophy: "We deliver what we promise, and we promise only what we can deliver." Beyond providing top-tier IT talent, R2 builds cutting-edge proprietary solutions like SmartEnt-an Enterprise AI & IoT Intelligence Platform utilizing advanced NLP and AI technologies. By partnering closely with our clients, we deliver technology-driven outcomes that are realistic, measurable, and impactful.
Job Summary: The shift from classical Machine Learning to Generative AI requires a new breed of infrastructure engineering. R2 Technologies is looking for an AI/ML Ops & Infrastructure Engineer to build and manage the operational backbone for our advanced LLM and agentic systems. You will transition beyond basic CI/CD to implement full-lifecycle LLMOps-managing foundation models, fine-tuned adapters, routing logic, and guardrails. Your work will ensure that our AI solutions, including SmartEnt, run with high performance, optimal GPU utilization, and rigorous compliance.
Key Responsibilities: * Design and maintain highly scalable LLMOps pipelines for continuous integration, evaluation, and deployment of machine learning models and AI agents.
  • Deploy and manage containerized AI applications and model inference servers (e.g., vLLM, Ray Serve, NVIDIA Triton) on Kubernetes across multi-cloud environments (AWS, GCP, Azure).
  • Implement comprehensive observability and trace-level logging for multi-step agentic workflows using platforms like LangSmith, W&B Weave, or MLflow.
  • Automate infrastructure provisioning and monitoring using tools like Terraform and agent-driven workflows (e.g., n8n, GitHub Actions).
  • Optimize GPU computing costs, latency, and token usage for high-traffic AI inference endpoints.
  • Enforce security guardrails, toxic output filtering, and robust access policies within the AI deployment infrastructure.
  • Actively utilize AI-assisted coding tools (Copilot, Cursor) to automate infrastructure-as-code (IaC) and streamline Kubernetes management.

Qualifications: *3 years of hands-on experience in MLOps, DevOps, Site Reliability Engineering (SRE), or Cloud Infrastructure.
  • Strong proficiency in containerization and orchestration (Docker, Kubernetes).
  • Experience with ML/LLM operational platforms (MLflow, Weights & Biases, Databricks Mosaic AI, or SageMaker).
  • Familiarity with serving open-source or fine-tuned LLMs and optimizing inference performance.
  • Proven experience or strong familiarity working alongside AI coding assistants to enhance productivity.
  • Scripting/programming skills in Python and bash, along with experience in CI/CD automation.
  • Passion for the evolving landscape of AI infrastructure, cost-optimization (FinOps), and system reliability.

Skills:
Nvidia,Infrastructure

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