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Tensorflow Pytorch Jobs in Phoenix, AZ (NOW HIRING)

TensorFlow, PyTorch, Scikit-learn * Hands-on experience with MLOps tools : MLflow, Kubeflow, Airflow, SageMaker, Azure ML * Knowledge of CI/CD tools : Jenkins, GitHub Actions, GitLab CI * Experience ...

Strong programming skills in Python, Bash, or Go, with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn). Preferred Certifications (one or more): Google Cloud Professional Machine ...

Strong coding skills in Python and/or Java, with experience in ML frameworks (TensorFlow, PyTorch, scikit-learn) * Experience building and scaling IVR/Conversational AI systems (Dialogflow, Contact ...

Java Developer with GenAI Copilot

Phoenix, AZ · On-site

$50.75 - $65.50/hr

Experience with machine learning frameworks (e.g., TensorFlow, PyTorch). * Familiarity with containerization and orchestration technologies (Docker, Kubernetes). * Knowledge of front-end technologies ...

Experience with TensorFlow, PyTorch, and scikit-learn. * Cloud Platforms : Working knowledge of Google Cloud and Azure. * Front-End Frameworks/Libraries : Experience with React, Angular, Vue.js, and ...

Machine learning frameworks (TensorFlow, PyTorch). * Generative AI technologies (LLMs, prompt engineering). * Vertex AI model lifecycle (development, deployment, monitoring). * Responsible AI ...

Experience with TensorFlow, PyTorch, and scikit-learn. * Cloud Platforms : Working knowledge of Google Cloud and Azure. * Front-End Frameworks/Libraries : Experience with React, Angular, Vue.js, and ...

Technical Skills: o Proficiency in Python and related libraries (e.g., TensorFlow, PyTorch, scikit-learn). o Expertise in chatbot development and NLP techniques. o Strong understanding of machine ...

And familiar with machine learning platforms Tensorflow, Pytorch, Mxnet, etc. * The basic algorithms and deep learning algorithms of related machine learning have a deep understanding and mastery ...

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Tensorflow Pytorch information

See Phoenix, AZ salary details

$37.2K

$121.9K

$195.1K

How much do tensorflow pytorch jobs pay per year?

As of Jun 19, 2026, the average yearly pay for tensorflow pytorch in Phoenix, AZ is $121,868.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,800.00 and $135,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Deep Learning Engineer specializing in TensorFlow and PyTorch, and why are they important?

To thrive as a Deep Learning Engineer with a focus on TensorFlow and PyTorch, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant degree. Proficiency in programming languages like Python, experience with TensorFlow and PyTorch frameworks, and familiarity with cloud platforms or GPU computing are essential. Analytical thinking, problem-solving, and effective communication are standout soft skills for collaborating with teams and interpreting model results. These skills are crucial for developing, deploying, and optimizing AI models that drive innovation and solve complex real-world problems.

What are TensorFlow and PyTorch?

TensorFlow and PyTorch are two of the most popular open-source deep learning frameworks used by researchers and developers to build, train, and deploy machine learning models. TensorFlow, developed by Google, offers robust support for production environments and has a large ecosystem. PyTorch, developed by Facebook, is known for its flexibility, ease of use, and dynamic computational graph, making it popular in academia and research. Both frameworks support a wide range of neural network architectures and are used extensively for tasks such as computer vision, natural language processing, and reinforcement learning.

What is the difference between Tensorflow Pytorch vs Data Scientist?

AspectTensorflow PytorchData Scientist
Required SkillsDeep learning frameworks, Python, machine learningData analysis, statistical skills, Python/R, machine learning
Work EnvironmentAI/ML development, research, software engineeringData analysis, reporting, business insights
Industry UsageAI/ML projects, research labs, tech companiesBusiness, finance, healthcare, tech

Tensorflow and Pytorch are deep learning frameworks used primarily by AI/ML developers, while Data Scientists utilize these tools for data analysis and modeling. Although their skill sets overlap, Tensorflow Pytorch focus on model development, whereas Data Scientists apply these models to derive insights and inform decisions.

How do TensorFlow/PyTorch engineers typically collaborate with data scientists and other team members in a production environment?

TensorFlow and PyTorch engineers often work closely with data scientists to transform experimental machine learning models into efficient, scalable production solutions. Collaboration involves frequent code reviews, shared development environments, and regular meetings to align model requirements with deployment constraints. Engineers also coordinate with DevOps teams to ensure smooth integration and monitoring of models in production. Strong communication skills and a willingness to iterate on solutions are essential for bridging the gap between research and real-world application.
What are popular job titles related to Tensorflow Pytorch jobs in Phoenix, AZ? For Tensorflow Pytorch jobs in Phoenix, AZ, the most frequently searched job titles are:
What job categories do people searching Tensorflow Pytorch jobs in Phoenix, AZ look for? The top searched job categories for Tensorflow Pytorch jobs in Phoenix, AZ are:
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Job description

Role Overview

We are looking for a skilled MLOps Engineer to design, deploy, and manage scalable machine learning pipelines in production. The role focuses on enabling seamless integration of ML models into enterprise systems with reliability, automation, and governance.


Key Responsibilities

  • Design and implement end-to-end ML pipelines from data ingestion to model deployment
  • Build and manage CI/CD pipelines for ML models (training, testing, deployment)
  • Automate model monitoring, retraining, and performance optimization
  • Collaborate with Data Scientists and Data Engineers for productionizing ML models
  • Ensure scalability, reliability, and security of ML systems
  • Manage model versioning, experiment tracking, and lifecycle management
  • Implement best practices for governance, compliance, and reproducibility

Key Skills & Expertise

  • Strong programming skills in Python
  • Experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn
  • Hands-on experience with MLOps tools: MLflow, Kubeflow, Airflow, SageMaker, Azure ML
  • Knowledge of CI/CD tools: Jenkins, GitHub Actions, GitLab CI
  • Experience with cloud platforms: AWS
  • Strong understanding of data pipelines, ETL processes, and distributed systems