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Tensorflow Pytorch Jobs in Kentucky (NOW HIRING)

AI Technical Product Manager

Louisville, KY

$160K - $185K/yr

Design and co-develop working prototypes and production-ready components using leading AI/ML tools and platforms (e.g., Python, TensorFlow, PyTorch, Hugging Face, LangChain, Azure AI, OpenAI)

AI Technical Product Manager

Louisville, KY

$160K - $185K/yr

Design and co-develop working prototypes and production-ready components using leading AI/ML tools and platforms (e.g., Python, TensorFlow, PyTorch, Hugging Face, LangChain, Azure AI, OpenAI)

AI Technical Product Manager

Louisville, KY

$160K - $185K/yr

Design and co-develop working prototypes and production-ready components using leading AI/ML tools and platforms (e.g., Python, TensorFlow, PyTorch, Hugging Face, LangChain, Azure AI, OpenAI)

AI Technical Product Manager

Louisville, KY · On-site

$160K - $185K/yr

Design and co-develop working prototypes and production-ready components using leading AI/ML tools and platforms (e.g., Python, TensorFlow, PyTorch, Hugging Face, LangChain, Azure AI, OpenAI)

AI Security Architect - Erlanger, KY

Erlanger, KY · On-site

$64 - $82.75/hr

... TensorFlow, PyTorch, Scikit-learn) and MLOps tools (MLflow, Kubeflow). • Familiarity with adversarial ML concepts and tools (Pyrit, IBM Adversarial Robustness Toolbox, CleverHans). • Proficiency ...

Data Engineer (Remote)

Canton, MA · On-site +1

$121K - $145K/yr

Integration of data pipelines with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch, or similar) * Understanding of model lifecycle concepts (training, validation, deployment, monitoring)

... TensorFlow, PyTorch, Scikit-learn) and MLOps tools (MLflow, Kubeflow, Azure Machine Learning, Databricks, or equivalent enterprise AI platforms).Familiarity with adversarial ML concepts and tools ...

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Showing results 1-20

Tensorflow Pytorch information

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 Kentucky? For Tensorflow Pytorch jobs in Kentucky, the most frequently searched job titles are:
What cities in Kentucky are hiring for Tensorflow Pytorch jobs? Cities in Kentucky with the most Tensorflow Pytorch job openings:
Data Scientist (AI/ML Modeling, Forecasting and Algorithms)

Data Scientist (AI/ML Modeling, Forecasting and Algorithms)

Yum! Brands

Louisville, KY • On-site

Full-time

Posted 5 days ago

New


Yum! Brands rating

3.9

Company rating: 3.9 out of 10

Based on 8 frontline employees who took The Breakroom Quiz


Job description

Job Summary:
Yum Crave AI is seeking a Data Scientist to support the development and implementation of machine learning models that address operational and business needs. In this role, you will work on moderately complex modeling assignments and collaborate with cross-functional teams to deliver scalable AI/ML solutions.
Responsibilities:
• Develop and implement machine learning models using established approaches (regression, classification, forecasting, optimization)
• Translate business and operational requirements into structured modeling problems with guidance from senior team members
• Perform feature engineering, model training, validation, and evaluation using defined best practices
• Contribute to improving existing models and pipelines
• Support the design and analysis of experiments (e.g., A/B tests, simulations) under guidance
• Monitor model performance and identify opportunities for improvement
• Apply standard processes to assess model quality, accuracy, and reliability
• Work with structured and semi-structured datasets to prepare data for modeling
• Write clean, maintainable Python and SQL code
• Partner with Data Engineering and ML Engineering to support model deployment
• Clearly communicate analytical findings to technical and non-technical stakeholders
• Document assumptions, methodologies, and results
• Build strong working relationships within the immediate team and cross-functional partners
Qualifications:
Required:
• Bachelor’s or Master’s degree in a quantitative field (e.g., Data Science, Statistics, Computer Science, Engineering, Mathematics)
• 2+ years of experience in data science or machine learning
• Experience applying machine learning techniques to real-world problems
• Proficiency in Python and SQL
• Understanding of model evaluation metrics and validation approaches
Preferred:
• Exposure to deep learning frameworks (e.g., TensorFlow, PyTorch)
• Experience working in cloud environments (AWS, GCP, or Azure)
• Familiarity with experimentation methodologies (A/B testing)
• Experience collaborating with engineering teams on production deployments
Company:
Yum! Brands is a quick-service restaurant brand that primarily operates the likes of KFC, Pizza Hut, and Taco Bell. Founded in 1997, the company is headquartered in Louisville, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

What Yum! Brands employees say

Pay

Benefits

Hours and flexibility

Workplace

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