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Tensorflow Pytorch Jobs in Frankfort, KY (NOW HIRING)

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Tensorflow Pytorch information

See Frankfort, KY salary details

$32.6K

$106.6K

$170.7K

How much do tensorflow pytorch jobs pay per year?

As of Aug 10, 2026, the average yearly pay for tensorflow pytorch in Frankfort, KY is $106,602.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,500.00 and $118,100.00 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?

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 job categories do people searching Tensorflow Pytorch jobs in Frankfort, KY look for? The top searched job categories for Tensorflow Pytorch jobs in Frankfort, KY are:
Tensorflow Pytorch jobs near you

Manufacturing Innovation Advanced Technology Engineer

TECKNOMIC LLC

Georgetown, KY • On-site

$65K - $84K/yr

Other

Posted 4 days ago

Job description

Please Read Before Applying

  • 5+ years in industrial machine vision and edge AI deployment
  • Proficiency in Python and C++ with ML frameworks (PyTorch, TensorFlow)
  • Experience integrating vision systems with PLCs and industrial protocols (OPC-UA, MQTT)
  • Hands-on Docker containerization and Kubernetes orchestration
  • Onsite in Georgetown, KY; able to travel internationally as needed

About the Role

A leading automotive manufacturer’s Manufacturing Innovation / Advanced Technology group is seeking an Advanced Technology Engineer to develop and deploy production-grade machine learning and computer vision models for industrial inspection across high-volume manufacturing lines. You will accelerate model development with synthetic data, deliver containerized software optimized for edge hardware, and integrate robust AI solutions into manufacturing systems to improve competitiveness.

Responsibilities

  • Design and implement computer vision models for defect detection, segmentation, and classification
  • Accelerate training cycles using synthetic data, active learning, and domain randomization
  • Package models/services with Docker and manage deployments through Kubernetes or equivalent orchestration
  • Implement version control, rollback, and observability for latency, drift, and false-positive/negative metrics
  • Optimize inference for edge/embedded hardware (e.g., NVIDIA Jetson, Intel accelerators) for real-time moving-line inspection
  • Ensure consistent performance under varying lighting, optics, and surface conditions
  • Integrate vision systems with PLCs, encoders, triggers, and industrial networks using OPC-UA, MQTT, and REST
  • Align deployments with plant-level connectivity and reliability standards
  • Lead data collection campaigns, manage annotation workflows, and establish quality gates for validation
  • Ensure uptime via proactive monitoring, calibration (MSA), drift detection, and root cause analysis
  • Lead and manage projects from concept to launch (schedules, punch lists, milestones)
  • Collaborate across manufacturing centers, corporate technical/R&D centers, IT, and automation teams

Required Qualifications

  • Bachelor’s degree in EE, ME, Computer Science, IT, or a related field
  • 5 years of experience in industrial machine vision and edge AI deployment
  • Python and C++ with strong knowledge of ML frameworks (PyTorch, TensorFlow)
  • Containerization (Docker) and orchestration (Kubernetes)
  • ONNX Runtime, TensorRT, and optimization for embedded hardware
  • Integrating vision systems with PLCs and industrial protocols (OPC-UA, MQTT)
  • Full model lifecycle: data collection, labeling, validation, rollout, monitoring, retraining
  • Object detection, classification, and segmentation (semantic/instance models)
  • Industrial cameras, lighting, optics, and trigger-based image capture
  • Balancing inspection accuracy with false positives vs. flow-out risk
  • Project management (scope, schedules, vendor/contractor management, status updates)
  • Ability to travel domestically and internationally (Canada, Mexico, Japan) as needed

Technical Skills

  • Python
  • C++
  • PyTorch
  • TensorFlow
  • Computer Vision (detection, segmentation, classification)
  • Edge AI (NVIDIA Jetson, Intel accelerators)
  • ONNX Runtime
  • TensorRT
  • Docker
  • Kubernetes
  • OPC-UA
  • MQTT
  • REST
  • PLC integration
  • Industrial cameras / optics
  • Synthetic data / domain randomization
  • MLOps

Preferred Qualifications

  • Master’s or advanced degree in engineering or a related field
  • Academic research experience in new technology
  • Project management involving internal and external parties (6+ months)
  • Equipment deployment including PFMEA and quality control plans
  • Deploying automotive production equipment
  • Robotics — operation, teaching, maintenance, and safety
  • Synthetic data generation (GANs, VAEs, NeRFs, Blender) and domain randomization
  • High-speed inline inspection and vision-based process control
  • IIoT data pipelines and messaging standards
  • Calibration, measurement system analysis (MSA), and quality-critical inspection

Must-Have Skills

  • Machine Vision
  • Edge AI
  • Computer Vision
  • Python
  • C++
  • PyTorch / TensorFlow
  • Docker / Kubernetes
  • PLC Integration
  • OPC-UA / MQTT
  • MLOps

Monster Skills List

  • Machine Vision
  • Computer Vision
  • Edge AI
  • Deep Learning
  • Machine Learning
  • Python
  • C++
  • PyTorch
  • TensorFlow
  • ONNX
  • TensorRT
  • Object Detection
  • Image Segmentation
  • Classification
  • Defect Detection
  • Docker
  • Kubernetes
  • NVIDIA Jetson
  • MLOps
  • Model Deployment
  • OPC-UA
  • MQTT
  • REST API
  • PLC
  • Industrial Automation
  • Industrial Cameras
  • Synthetic Data
  • Domain Randomization
  • GANs
  • MSA
  • IIoT

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