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Tensorflow Pytorch Jobs in Arizona (NOW HIRING)

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 ...

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 ...

AI Architect

Phoenix, AZ · On-site

$60 - $68/hr

AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn) * Generative AI / LLMs (OpenAI, Azure OpenAI, etc.) * Expertise in application architecture and system design * Experience in: * Cloud platforms ...

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 ...

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 ...

Familiarity with machine learning frameworks and libraries like TensorFlow, PyTorch, or Hugging Face. * Strong analytical and problem-solving skills with a keen eye for detail. * Excellent ...

TensorFlow, PyTorch, Scikit-learn, MLflow * Epic Clarity, Epic Caboodle, FHIR, HL7 * Claude Code, GitHub Copilot, Cursor * Git, Azure DevOps, CI/CD, Docker, Kubernetes Company Description MY DR NOW ...

AI Architect

Phoenix, AZ · On-site

$62.50 - $82.50/hr

Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, and Scikit-learn. * Strong experience with Generative AI / LLMs , including OpenAI, Azure OpenAI, or similar technologies.

... TensorFlow, PyTorch and other deep learning libraries • Experience with cloud technologies (AWS, Azure, GCP) • Strong understanding of cybersecurity principles as they apply to software ...

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Tensorflow Pytorch information

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 are the key skills and qualifications needed to thrive as a deep learning engineer specializing in TensorFlow and PyTorch?

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.

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 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.

What are popular job titles related to Tensorflow Pytorch jobs in Arizona?

For Tensorflow Pytorch jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Tensorflow Pytorch jobs in Arizona look for?

The top searched job categories for Tensorflow Pytorch jobs in Arizona are:

What cities in Arizona are hiring for Tensorflow Pytorch jobs?

Cities in Arizona with the most Tensorflow Pytorch job openings:

AI/ML Engineer

Programmers.io

Scottsdale, AZ • On-site

Contractor

Re-posted 13 days ago


Job description

Key Responsibilities:
Design, implement, and maintain ML pipelines for training, testing, and deploying AIML models.
Manage and optimize cloud-based ML infrastructure (GCP Vertex AI, AWS SageMaker, or equivalent).
Implement CICD pipelines for ML and AI-driven applications.
Monitor, troubleshoot, and optimize model performance and system reliability.
Automate workflows for data ingestion, model training, deployment, and monitoring.
Collaborate with cross-functional teams to ensure secure, scalable, and compliant ML operations.
Apply MLOps best practices for reproducibility, versioning, and governance of ML models.
Required Qualifications:
5 years experience in DevOps, CloudOps, or ML Ops.
5 years experience with GCP AIML services (Vertex AI, AI Platform, BigQuery ML) or AWS ML services (SageMaker etc).
5 years Experience with containerization and orchestration (Docker, Kubernetes).
Proficiency in infrastructure-as-code (Terraform, CloudFormation, or Deployment Manager). Familiarity with CICD pipelines (Jenkins, GitHub Actions, GitLab CI, or ArgoCD).
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 Learning Engineer
Google Cloud Professional Data Engineer
AWS Certified Machine Learning Specialty
Certified Kubernetes Admin(CKA)
Google Professional Cloud Architect