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Ml Inference Jobs in Texas (NOW HIRING)

This role is pivotal in enabling enterprise-scale ML and generative AI capabilities by building ... Design highly available and performant serving environments for LLM inference using Azure ...

... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...

... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...

... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...

... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...

... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...

Ai/ML Engineer

Dallas, TX · On-site

$85K - $107K/yr

This role is pivotal in enabling enterprise-scale ML and generative AI capabilities by building ... Design highly available and performant serving environments for LLM inference using Azure ...

Ai/ML Engineer

Dallas, TX · On-site

$85K - $107K/yr

This role is pivotal in enabling enterprise-scale ML and generative AI capabilities by building ... Design highly available and performant serving environments for LLM inference using Azure ...

... inference, and monitoring in production environments. • Participate in continuous improvement of the ML infrastructure and processes for scalability and performance. Qualifications : Required : • ...

Core Responsibilities (AI/ML, Python, AWS, GenAI) * Design and implement end-to-end AI/ML and ... Build robust MLOps workflows, including model versioning, containerized training/inference ...

Enable early firmware, driver, runtime, and ML stack bring-up on emulated hardware * Support execution of AI inference workloads (e.g., CNNs, transformers) on emulated Mythic accelerators

Enable early firmware, driver, runtime, and ML stack bring-up on emulated hardware * Support execution of AI inference workloads (e.g., CNNs, transformers) on emulated Mythic accelerators

Enable early firmware, driver, runtime, and ML stack bring-up on emulated hardware * Support execution of AI inference workloads (e.g., CNNs, transformers) on emulated Mythic accelerators

Preferred : • Financial domain expertise (risk, fraud, forecasting, customer intelligence). • Advanced ML topics: time series, graph ML, optimization, causal inference. • ONNX/TensorRT model ...

Development and Implement data pipelines and ML pipelines to facilitate model inference (both Real-time and batch) * Analyze large, complex data sets to identify the most performant way to process ...

Showing results 21-40

Ml Inference information

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 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 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.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.

What job categories do people searching Ml Inference jobs in Texas look for?

The top searched job categories for Ml Inference jobs in Texas are:

What cities in Texas are hiring for Ml Inference jobs?

Cities in Texas with the most Ml Inference job openings:

Infographic showing various Ml Inference job openings in Texas as of August 2026, with employment types broken down into 44% Full Time, 11% Part Time, and 45% Contract. Highlights an 89% In-person, and 11% Remote job distribution.

$85K - $107K/yr

Full-time

Re-posted 26 days ago


Johnson Controls rating

8.0

Company rating: 8.0 out of 10

Based on 409 frontline employees who took The Breakroom Quiz

136th of 540 rated manufacturers


Job description

Johnson Controls International (JCI) is looking for a Machine Learning / Platform Engineer to join our growing AI and Data Platform team. This role is pivotal in enabling enterprise-scale ML and generative AI capabilities by building secure, scalable, and automated infrastructure on Azure using Terraform and Azure DevOps.

You'll work at the intersection of ML, DevOps, and cloud engineering-building the foundation that supports real-time LLM inference, retraining, orchestration, and integration across JCI's product and operations landscape.

How you will do it

ML Platform Engineering & MLOps (Azure-Focused)

  • Build and manage end-to-end ML/LLM pipelines on Azure ML using Azure DevOps for CI/CD, testing, and release automation.

  • Operationalize LLMs and generative AI solutions (e.g., GPT, LLaMA, Claude) with a focus on automation, security, and scalability.

  • Develop and manage infrastructure as code using Terraform, including provisioning compute clusters (e.g., Azure Kubernetes Service, Azure Machine Learning compute), storage, and networking.

  • Implement robust model lifecycle management (versioning, monitoring, drift detection) with Azure-native MLOps components.

Infrastructure & Cloud Architecture

  • Design highly available and performant serving environments for LLM inference using Azure Kubernetes Service (AKS) and Azure Functions or App Services.

  • Build and manage RAG pipelines using vector databases (e.g., Azure Cognitive Search, Redis, FAISS) and orchestrate with tools like LangChain or Semantic Kernel.

  • Ensure security, logging, role-based access control (RBAC), and audit trails are implemented consistently across environments.

Automation & CI/CD Pipelines

  • Build reusable Azure DevOps pipelines for deploying ML assets (data pre-processing, model training, evaluation, and inference services).

  • Use Terraform to automate provisioning of Azure resources, ensuring consistent and compliant environments for data science and engineering teams.

  • Integrate automated testing, linting, monitoring, and rollback mechanisms into the ML deployment pipeline.

Collaboration & Enablement

  • Work closely with Data Scientists, Cloud Engineers, and Product Teams to deliver production-ready AI features.

  • Contribute to solution architecture for real-time and batch AI use cases, including conversational AI, enterprise search, and summarization tools powered by LLMs.

  • Provide technical guidance on cost optimization, scalability patterns, and high-availability ML deployments.

Qualifications & Skills

Required Experience

  • Bachelor's or Master's in Computer Science, Engineering, or a related field.

  • 5+ years of experience in ML engineering, MLOps, or platform engineering roles.

  • Strong experience deploying machine learning models on Azure using Azure ML and Azure DevOps.

  • Proven experience managing infrastructure as code with Terraform in production environments.

Technical Proficiency

  • Proficiency in Python (PyTorch, Transformers, LangChain) and Terraform, with scripting experience in Bash or PowerShell.

  • Experience with Docker and Kubernetes, especially within Azure (AKS).

  • Familiarity with CI/CD principles, model registry, and ML artifact management using Azure ML and Azure DevOps Pipelines.

  • Working knowledge of vector databases, caching strategies, and scalable inference architectures.

Soft Skills & Mindset

  • Systems thinker who can design, implement, and improve robust, automated ML systems.

  • Excellent communication and documentation skills-capable of bridging platform and data science teams.

  • Strong problem-solving mindset with a focus on delivery, reliability, and business impact.

Preferred Qualifications

  • Experience with LLMOps, prompt orchestration frameworks (LangChain, Semantic Kernel), and open-weight model deployment.

  • Exposure to smart buildings, IoT, or edge-AI deployments.

  • Understanding of governance, privacy, and compliance concerns in enterprise GenAI use cases.

  • Certification in Azure (e.g., Azure Solutions Architect, Azure AI Engineer, Terraform Associate) is a plus.

HIRING SALARY RANGE: $85,000 - 107,000 (Salary to be determined by the education, experience, knowledge, skills, andabilities of the applicant, internal equity, location and alignment with market data.) This position includes acompetitive benefits package. For details, please visit the About Us tab on the Johnson Controls Careers siteat https://jobs.johnsoncontrols.com/about-us

Johnson Controls International plc. is an equal employment opportunity and affirmative action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, protected veteran status, genetic information, sexual orientation, gender identity, status as a qualified individual with a disability or any other characteristic protected by law. To view more information about your equal opportunity and non-discrimination rights as a candidate, visit EEO is the Law. If you are an individual with a disability and you require an accommodation during the application process, please visit here.


What Johnson Controls employees say

Pay

Benefits

Hours and flexibility

Workplace

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About Johnson Controls

Sourced by ZipRecruiter

Johnson Controls is a world leader in smart buildings, creating safe, healthy and sustainable spaces. For nearly 140 years, we’ve made buildings better and now we’re transforming them again with our award-winning digital technologies and services. We’re using artificial intelligence and data driven solutions to give you deeper insight into your building’s health, sustainability and performance. It’s changing the way we design, operate and maintain indoor environments and driving to a new era of autonomous buildings. We deliver the blueprint of the future for industries such as healthcare, schools, data centers, airports, stadiums, hotels, manufacturing and beyond through OpenBlue, our comprehensive suite of connected solutions. Johnson Controls offers the world’s largest portfolio of building technology, software and services. Supported by a team of more than 100,000 dedicated employees working across 150 countries, we’re helping customers achieve their sustainability goals and power their mission.

Industry

Machinery manufacturing, water transportation, public safety statistics centers and offices and manufacturing

Company size

10,000+ Employees

Headquarters location

Milwaukee, WI, US