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Tensorflow Pytorch Jobs in Delaware (NOW HIRING)

Athena LLC

TensorFlow, PyTorch, scikit-learn) and experience training or fine-tuning models. * Prior exposure to recommendation algorithms or matching systems through coursework or projects. * Familiarity with ...

Athenago

Data Science Intern

Wilmington, DE

TensorFlow, PyTorch, scikit-learn) and experience training or fine-tuning models. * Prior exposure to recommendation algorithms or matching systems through coursework or projects. * Familiarity with ...

ClifyX

GenAI Engineer

Wilmington, DE · On-site

TensorFlow, PyTorch • GenAI Tools: LangChain, LlamaIndex • Vector DB: Pinecone, FAISS • Cloud Technologies: AWS / Azure / GCP • Data Pipelines: ETL/ELT, Real-time & Batch Processing • ...

Berkley

Principal Engineer - AI Platform

Wilmington, DE

$131K - $175K/yr

Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn * Multiple years working with structured and unstructured data within a cloud-based data ...

JPMorgan Chase & Co

CCB Risk Program Associate

Wilmington, DE · On-site

$57K - $57K/yr

Proficiency in Python, TensorFlow, PyTorch, Spark, or Scala, coupled with experience in big data technologies such as Hadoop, AWS, and Hive, and familiarity with MLOps tooling that supports model ...

Sallie_mae

Senior Manager, Statistical Modeling

Newark, DE

$85K - $104K/yr

Proficiency in statistical programming languages such as Python, R, or SAS, and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). * Strong understanding of statistical modeling ...

Sallie Mae

Senior Manager, Statistical Modeling

Newark, DE · On-site

$85K - $104K/yr

Proficiency in statistical programming languages such as Python, R, or SAS, and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). * Strong understanding of statistical modeling ...

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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 Delaware? For Tensorflow Pytorch jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching Tensorflow Pytorch jobs in Delaware look for? The top searched job categories for Tensorflow Pytorch jobs in Delaware are:
What cities in Delaware are hiring for Tensorflow Pytorch jobs? Cities in Delaware with the most Tensorflow Pytorch job openings:
Tensorflow Pytorch jobs near you
Infographic showing various Tensorflow Pytorch job openings in Delaware as of July 2026, with employment types broken down into 43% Internship, and 57% Full Time. Highlights an 100% In-person job distribution.

GenAI Engineer

Tata Consultancy Service Limited

Wilmington, DE • On-site

$100K - $110K/yr

Full-time

Re-posted 11 days ago

Job description

GenAI Engineer
Design and develop AI/ML and Generative AI solutions for banking use cases including fraud detection, risk modeling, and customer analytics.
• Build, fine-tune, and deploy ML models and LLMs for credit scoring, AML, and automation
• Implement RAG-based GenAI applications using internal banking data
• Develop scalable data pipelines for training, validation, and real-time inference
• Collaborate with risk, compliance, finance, and business teams for AI solutions
• Ensure regulatory compliance and AI governance standards
• Implement data security, privacy, and access control mechanisms
• Integrate AI models into production using APIs and microservices
• Apply prompt engineering and model optimization techniques
• Monitor model performance, drift detection, and continuous improvement
• Develop explainable AI (XAI) for transparent decision-making
• Optimize cost, latency, and scalability of AI systems
• Troubleshoot AI/ML system issues across data and deployment layers
• Write efficient Python code using AI frameworks
• Follow MLOps best practices (CI/CD, automated deployment)
• Ensure responsible AI practices (bias, fairness, ethics)
• Mentor teams and contribute to enterprise AI platforms.
• Languages: Python
• AI/ML & GenAI: Machine Learning, Deep Learning, LLMs, Prompt Engineering, Fine-tuning
• Frameworks: TensorFlow, PyTorch
• GenAI Tools: LangChain, LlamaIndex
• Vector DB: Pinecone, FAISS
• Cloud Technologies: AWS / Azure / GCP
• Data Pipelines: ETL/ELT, Real-time & Batch Processing
• Integration: APIs, Microservices
• Concepts: RAG Architecture, XAI, Model Optimization
• Methodologies: Agile/Scrum, MLOps (CI/CD, Model Versioning, Deployment)
• Compliance: Banking regulations (SR 11-7, GDPR), Model Risk Management
• Soft Skills: Strong communication, stakeholder management, and analytical thinking
Salary Range- $100,000-$110,000 a year
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