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Ml Inference Jobs in Canton, GA (NOW HIRING)

Falcomm

AI/CAD Software Engineer

Atlanta, GA

Seamlessly integrate ML inference engines into existing RF EDA infrastructure to provide real-time feedback to PA designers. * Software Engineering Excellence: Establish best practices including ...

Honeywell

Sr Advanced AI Platform Engineer

Atlanta, GA · On-site

$117K - $155K/yr

KEY RESPONSIBILITIES AI/ML Platform Engineering * Develop high-performance, production-ready Python APIs using FastAPI to serve as the primary interface for on-device model inference * Design, build ...

Honeywell

Sr Advanced AI Platform Engineer

Atlanta, GA · On-site

$117K - $155K/yr

KEY RESPONSIBILITIES AI/ML Platform Engineering * Develop high-performance, production-ready Python APIs using FastAPI to serve as the primary interface for on-device model inference * Design, build ...

Equifax, Inc.

Senior Applied AI Engineer

Alpharetta, GA · On-site

$100K - $138K/yr

Lead the end-to-end design of AI systems including LLM-powered applications, NLP pipelines, and ML inference infrastructure at enterprise scale. * Evaluate and recommend AI frameworks, cloud services ...

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All JobsMl Inference JobsMl Inference Jobs in Canton, GA

Ml Inference information

See Canton, GA salary details

$35.4K

$115.9K

$185.5K

How much do ml inference jobs pay per year?

As of Jul 4, 2026, the average yearly pay for ml inference in Canton, GA is $115,888.00, according to ZipRecruiter salary data. Most workers in this role earn between $93,000.00 and $128,400.00 per year, depending on experience, location, and employer.

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.

Which 3 jobs will survive AI?

Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.

What are some common challenges faced by ML Inference Engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.
What are popular job titles related to Ml Inference jobs in Canton, GA? For Ml Inference jobs in Canton, GA, the most frequently searched job titles are:
What cities near Canton, GA are hiring for Ml Inference jobs? Cities near Canton, GA with the most Ml Inference job openings:
Ml Inference jobs near you
Java Backend Engineer (AI & ML) - Q125

Java Backend Engineer (AI & ML) - Q125

R2 Technologies Corporation

Alpharetta, GA • On-site

Full-time

Medical, Retirement, PTO

Posted 29 days ago

Job description

Overview:
R2 Technologies Corporation (R2), headquartered in Alpharetta, GA, is a leading IT services provider specializing in Java, .NET, Big Data, Cloud Computing (AWS, GCP, Azure), Artificial Intelligence (AI), Machine Learning (ML), software development, project management, SAP, and enterprise resource planning (ERP). We empower clients-from startups to Fortune 1000 companies-with scalable, platform-based solutions and data-driven insights using modern cloud technologies. Our commitment to blending highly skilled talent with innovative productivity platforms ensures rapid delivery of business value, making us one of the most respected and trusted technology companies in the United States. At R2, we're passionate about driving operational excellence and competitive advantage for our clients through cutting-edge AI, ML, and cloud solutions. Join our team and help shape the future of technology innovation!
Java Backend Engineer (AI & ML)
Location: Alpharetta, GA (willing to travel to client locations)
Employment Type: Full-Time (W2)
Role Overview
We are seeking a dedicated Java Backend Engineer to build and enhance backend systems with integrated AI and ML capabilities. This role focuses on developing robust Java-based solutions that leverage machine learning models and data pipelines for intelligent applications.
Key Responsibilities
  • Develop and maintain Java-based backend systems to support AI and ML-driven functionalities.
  • Integrate machine learning models into applications using REST APIs for seamless model inference.
  • Build and manage data pipelines to preprocess and feed data into ML models for real-time or batch processing.
  • Implement scalable RESTful APIs to facilitate communication between backend systems and AI/ML services.
  • Collaborate with data scientists to optimize model integration and ensure efficient deployment in production.
  • Ensure backend systems are secure, performant, and reliable while handling AI/ML workloads.

Required Qualifications
  • Bachelor's degree in Computer Science, Software Engineering, or a related field (or equivalent experience).
  • 3 years of experience in Java backend development with a focus on integrating AI or machine learning solutions.
  • Proficiency in building REST APIs and managing data pipelines for ML model integration.
  • Experience with frameworks like Spring Boot for developing scalable backend services.
  • Strong understanding of machine learning workflows and API-based model consumption.

Preferred Qualifications
  • Familiarity with ML frameworks like TensorFlow, PyTorch, or Scikit-learn for backend integration.
  • Experience with cloud-based data pipelines (e.g., AWS Glue, Azure Data Factory) for ML preprocessing.
  • Knowledge of event-driven architectures using tools like Kafka to support real-time ML inference.

Compensation & Benefits
  • Competitive salary and comprehensive benefits package (healthcare, PTO, 401k).
  • Opportunities for professional growth and upskilling in AI and cloud technologies.

R2 Technologies Corporation is an equal opportunity employer and values diversity in the workplace.
Skills:
Java, Backend, AI, Machine Learning, REST APIs, Data Pipelines, Model Integration, Spring Boot

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