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Ml Inference Jobs in Garland, TX (NOW HIRING)

Programmers.io

Java with AI ML ENgineer

Dallas, TX · On-site

$51.25 - $70.25/hr

Familiarity with ML model lifecycle - from data ingestion, training, deployment, to real-time inference (MLOPS) * 2+ years hands-on experience with GCP, AWS, or Azure * 2+ years working with pub/sub ...

Public Storage

Senior AI/ML Data Engineer

Frisco, TX · On-site

$99K - $134K/yr

Deploy and manage training and inference workflows on GCP using Cloud Run, GKE, and Vertex AI * Implement CI/CD, model versioning, rollback strategies, and operational guardrails for ML systems

Public Storage

Senior AI/ML Data Engineer

Frisco, TX

$99K - $134K/yr

Deploy and manage training and inference workflows on GCP using Cloud Run, GKE, and Vertex AI * Implement CI/CD, model versioning, rollback strategies, and operational guardrails for ML systems

Public Storage

Senior AI/ML Data Engineer

Frisco, TX · On-site

$99K - $134K/yr

Deploy and manage training and inference workflows on GCP using Cloud Run, GKE, and Vertex AI * Implement CI/CD, model versioning, rollback strategies, and operational guardrails for ML systems

Prophecy Technologies

AI/ML Engineer

Plano, TX · On-site

$109K - $131K/yr

AI/ML Engineer ?? Location: Plano, TX (Hybrid) ?? Duration: Long-Term Contract Client: EmergerTech ... Optimize model accuracy, scalability, inference performance, and latency. * Implement AI governance ...

Public Storage

Senior AI/ML Data Engineer

Frisco, TX · On-site

$99K - $134K/yr

Deploy and manage training and inference workflows on GCP using Cloud Run, GKE, and Vertex AI * Implement CI/CD, model versioning, rollback strategies, and operational guardrails for ML systems

Tiger Analytics Inc.

ML Ops Architect

Dallas, TX · Remote

As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers ... inference at scale. * Deploy and manage machine learning & data pipelines in production ...

Tiger Analytics Inc.

ML Ops Architect

Dallas, TX · On-site

As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers ... inference at scale. * Deploy and manage machine learning & data pipelines in production ...

Tiger Analytics Inc.

ML Ops Architect

Dallas, TX · On-site +1

As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers ... inference at scale. * Deploy and manage machine learning & data pipelines in production ...

Texas Instruments

AI/ML Engineer

Richardson, TX · On-site

... inference pipelines under technical guidance * Work with GenAI & LLMs - contribute to LLM-based ... Advanced AI/ML capabilities - experience building with LLMs or generative AI APIs; familiar with ...

3B Staffing LLC

AWS DevOps Engineer with AI/ML

Murphy, TX · On-site

$50.50 - $69/hr

AI/ML knowledge (2-3 years) * AWS * Observability tools - Grafana, Slunk, Dynatrace * Automation ... time inference use cases. * Implement and operate MLOps capabilities (deployment patterns ...

3B Staffing LLC

AWS DevOps Engineer with AI/ML

Murphy, TX · On-site

$50.50 - $69/hr

AI/ML knowledge (2-3 years) * AWS * Observability tools - Grafana, Slunk, Dynatrace * Automation ... time inference use cases. * Implement and operate MLOps capabilities (deployment patterns ...

Vizient

Sr AI/ML Engineer

Irving, TX

$102K - $179K/yr

Own AI/ML solutions end to end, from scoping and design through implementation, deployment, and ... Experience optimizing cost and performance for large-scale inference workloads preferred.

Vizient, Inc.

Sr AI/ML Engineer

Irving, TX · On-site

$102K - $179K/yr

Own AI/ML solutions end to end, from scoping and design through implementation, deployment, and ... Experience optimizing cost and performance for large-scale inference workloads preferred.

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All JobsMl Inference JobsMl Inference Jobs in Garland, TX

Ml Inference information

See Garland, TX salary details

$36.2K

$118.6K

$189.9K

How much do ml inference jobs pay per year?

As of Sep 6, 2026, the average yearly pay for ml inference in Garland, TX is $118,587.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,200.00 and $131,400.00 per year, depending on experience, location, and employer.

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 are the key skills and qualifications needed to thrive in ML inference?

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 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.

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 cities near Garland, TX are hiring for Ml Inference jobs?

Cities near Garland, TX with the most Ml Inference job openings:

Ml Inference jobs near you
Infographic showing various Ml Inference job openings in Garland, TX as of August 2026, with employment types broken down into 91% Full Time, 4% Part Time, and 5% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $118,587 per year, or $57 per hour.

Java with AI ML ENgineer

Programmers.io

Dallas, TX • On-site

$51.25 - $70.25/hr

Contractor

Re-posted 17 days ago

Job description

Job Description:

Responsibilities

  • Develop and maintain backend microservices using Python, Java and Spring Boot
  • Build and integrate APIs (both GraphQL and REST) for scalable service communication
  • Deploy and manage services on Google Cloud Platform (GKE)
  • Work with Google Cloud Spanner (Postgres dialect) and pub/sub tools like Confluent Kafka (or similar)
  • Automate CI/CD pipelines using GitHub Actions and Argo CD
  • Design and implement AI-driven microservices
  • Collaborate with Data Scientists and MLOps teams to integrate ML Models
  • Implement NLP pipelines
  • Enable continuous learning and model retraining workflows using Vertex AI or Kubeflow on GCP
  • Enable observability and reliability of AI decisions by logging model predictions, confidence scores and fallbacks into data lakes or monitoring tools

Required Qualifications

  • 5+ years of backend development experience with Java and Spring Boot
  • 2+ years working with APIs (GraphQL and REST) in microservices architectures
  • 2+ years’ experience integrating or consuming ML/AI models in production environments (e.g. RESTful ML APIs, TensorFlow Serving or Vertex AI Endpoints)
  • Experience working with structured and unstructured data (e.g. Rx Claim metadata, clinical documents, NLP processing).
  • Familiarity with ML model lifecycle - from data ingestion, training, deployment, to real-time inference (MLOPS)
  • 2+ years hands-on experience with GCP, AWS, or Azure
  • 2+ years working with pub/sub tools like Kafka or similar
  • 2+ years’ experience with databases (Postgres or similar)
  • 2+ years’ experience with CI/CD tools (GitHub Actions, Jenkins, Argo CD, or similar)

Preferred Qualifications

  • Hands-on experience with Google Cloud Platform
  • Familiarity with Kubernetes concepts; experience deploying services on GKE is a plus
  • Strong understanding of microservice best practices and distributed systems
  • Familiarity with Vertex AI, Kubeflow or similar AI platforms on GCP for model training and serving
  • Understanding of GenAI use cases, LLM prompt engineering and agentic orchestration (e.g. LangChain, transformers)
  • Experience deploying Python-based ML Services into Java microservice ecosystems (via REST, gRPC or sidecar patterns)
  • Knowledge of claim adjudication, Rx domain logic or healthcare specific workflow automation