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Ml Inference Jobs in Dallas, TX (NOW HIRING)

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 ...

Futran Tech Solutions Pvt. Ltd.

Python AI/ML

Plano, TX · On-site

$60/hr

Python AI/ML Engineer Location: - Onsite- Plano, TX Experience: - 10+Years Mandatory skills • Gen ... Hands-on with Strands Agent Framework Experience with AWS Bedrock (model access, inference ...

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.

Winaxis

AI/ML Engineer

Dallas, TX · On-site

$113K - $136K/yr

About the Role:- We are seeking a talented and innovative AI/ML Engineer to design, develop, and ... Build and maintain scalable data pipelines for model training and inference. Develop AI-powered ...

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All JobsMl Inference JobsMl Inference Jobs in Dallas, TX

Ml Inference information

See Dallas, TX salary details

$37.1K

$121.4K

$194.4K

How much do ml inference jobs pay per year?

As of Jul 17, 2026, the average yearly pay for ml inference in Dallas, TX is $121,417.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,400.00 and $134,500.00 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.
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What cities near Dallas, TX are hiring for Ml Inference jobs? Cities near Dallas, TX with the most Ml Inference job openings:
Ml Inference jobs near you
ML Ops Architect

ML Ops Architect

Tiger Analytics Inc.

Dallas, TX • On-site, Remote

Full-time

Re-posted 20 days ago

Job description

Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
We are looking for a motivated and passionate Machine Learning Engineers for our team.
Job Description:
As a Senior ML OPS Engineer, you will be joining a team of experienced Machine Learning Engineers that support, build, and enable Machine capabilities across the organization. You will work closely with internal customers and infrastructure teams to build our next generation data science workbench and ML platform and products. You will be able to further expand your knowledge and develop your expertise in modern Machine Learning frameworks, libraries and technologies while working closely with internal stakeholders to understand the evolving business needs. If you have a penchant for creative solutions and enjoy working in a hands-on, collaborative environment, then this role is for you.
Requirements
What you'll do in the role:
  • Implement scalable and reliable systems leveraging cloud-based architectures, technologies and platforms to handle model inference at scale.
  • Deploy and manage machine learning & data pipelines in production environments.
  • Work on containerization and orchestration solutions for model deployment.
  • Participate in fast iteration cycles, adapting to evolving project requirements.
  • Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.
  • Leverage CICD best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
  • Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
  • Collaborate with Data scientists, software engineers, data engineers, and other stakeholders to develop and implement best practices for MLOps, including CI/CD pipelines, version control, model versioning, monitoring, alerting and automated model deployment.
  • Manage and monitor machine learning infrastructure, ensuring high availability and performance.
  • Implement robust monitoring and logging solutions for tracking model performance and system health.
  • Monitor real-time performance of deployed models, analyze performance data, and proactively identify and address performance issues to ensure optimal model performance.
  • Troubleshoot and resolve production issues related to ML model deployment, performance, and scalability in a timely and efficient manner.
  • Implement security best practices for machine learning systems and ensure compliance with data protection and privacy regulations.
  • Collaborate with platform engineers to effectively manage cloud compute resources for ML model deployment, monitoring, and performance optimization.
  • Develop and maintain documentation, standard operating procedures, and guidelines related to MLOps processes, tools, and best practices.

Basic Qualifications:
  • Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
  • Typically requires 7+ years of hands-on work experience developing and applying advanced analytics solutions in a corporate environment with at least 4 years of experience programming with Python.
  • At least 3 years of experience designing and building data-intensive solutions using distributed computing.
  • At least 3 years of experience productionizing, monitoring, and maintaining models

Must have skills:
  • Understanding of Azure stack like Azure Machine Learning, Azure Data Factory, Azure Databricks, Azure Kubernetes Service, Azure Monitor, etc.
  • Demonstrated expertise in building and deploying AI/Machine Learning solutions at scale leveraging cloud such as AWS, Azure, or Google Cloud Platform.
  • Experience in developing and maintaining APIs (e.g.: REST).
  • Experience specifying infrastructure and Infrastructure as a code (e.g.: Ansible, Terraform).
  • Experience in designing, developing & scaling complex data & feature pipelines feeding ML models and evaluating their performance.
  • Ability to work across the full stack and move fluidly between programming languages and MLOps technologies (e.g.: Python, Spark, DataBricks, Github, MLFlow, Airflow).
  • Expertise in Unix Shell scripting and dependency-driven job schedulers.
  • Understanding of security and compliance requirements in ML infrastructure.
  • Experience with visualization technologies (e.g.: RShiny, Streamlit, Python DASH, Tableau, PowerBI).
  • Familiarity with data privacy standards, methodologies, and best practices.

Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

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