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Gpt Engineer Jobs (NOW HIRING)

Virtual Networx

Gen AI/ ML Engineer

Atlanta, GA · Hybrid

Job Role : Gen AI/ ML Engineer Location: Atlanta, GA Duration:6 months GPT, LLaMA, Claude, BERT, PEFT/LoRA, LangChain, RAG Pipelines, Vector Databases, Semantic Search, Azure, Docker Skills:

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StarsHR Inc

DevOps Engineer

Harrisburg, PA

$52 - $71.25/hr

Support GPT-enabled solution capabilities, including prompt engineering, intelligent chatbots, document summarization, semantic search, and related business use cases. Apply awareness of token usage ...

Logix Guru

Cloud Engineer

Mechanicsburg, PA · On-site

$52.75 - $70.50/hr

This role also requires the ability to support applied AI solution patterns, including GPT model integration, prompt engineering, and enterprise use cases such as summarization, semantic search, and ...

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Gpt Engineer information

What are the key skills and qualifications needed to thrive as a GPT Engineer, and why are they important?

To thrive as a GPT Engineer, you need a solid background in machine learning, natural language processing (NLP), and proficiency in programming languages such as Python, typically supported by a degree in computer science or a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, as well as experience with large language models (LLMs) and cloud platforms, is highly valuable. Strong problem-solving, communication, and collaboration skills help you navigate complex projects and effectively work within interdisciplinary teams. These skills and qualifications are crucial for developing, fine-tuning, and deploying advanced AI models that meet real-world needs and ensure ethical, high-performance outcomes.

How do GPT Engineers typically collaborate with data scientists and product teams during the model development process?

GPT Engineers frequently work alongside data scientists to refine training datasets, evaluate model outputs, and implement prompt engineering strategies. They also coordinate with product teams to understand user requirements, integrate GPT models into applications, and ensure a seamless user experience. Regular cross-functional meetings and collaborative problem-solving are standard, making strong communication skills essential for success in this role.

What is a GPT Engineer?

A GPT Engineer is a specialist who designs, develops, and optimizes applications using Generative Pre-trained Transformers (GPT) like those created by OpenAI. Their work involves integrating GPT models into software products, fine-tuning models for specific business needs, and ensuring the responsible and efficient use of AI technologies. GPT Engineers often collaborate with data scientists, product managers, and other developers to build innovative AI-driven solutions across various industries.

What is the difference between Gpt Engineer vs AI Developer?

AspectGpt EngineerAI Developer
Required CredentialsKnowledge of NLP, machine learning, programming skillsComputer science degree, programming, machine learning expertise
Work EnvironmentFocus on language models, API integration, fine-tuning GPT modelsBroader AI projects, including computer vision, robotics, and data analysis
Industry UsagePrimarily in tech, startups, and companies utilizing NLP solutionsWide range of industries including tech, healthcare, finance, and research

Gpt Engineers specialize in developing and fine-tuning language models like GPT, focusing on NLP applications. AI Developers have a broader scope, working on various AI technologies across multiple industries. While Gpt Engineers focus on language-specific AI, AI Developers handle diverse AI projects, making Gpt Engineer a specialized role within the larger AI development field.

More about Gpt Engineer jobs
What states have the most Gpt Engineer jobs? States with the most job openings for Gpt Engineer jobs include:
What job categories do people searching Gpt Engineer jobs look for? The top searched job categories for Gpt Engineer jobs are:
Gpt Engineer jobs near you

Azure + AWS Cloud Engineer (GPT Model Integration, Prompt Engineering)

S R INTERNATIONAL INC

Harrisburg, PA • Hybrid

$59 - $69/hr

Full-time

Posted 24 days ago

Job description

Job Title: Azure and AWS Cloud Engineer (GPT Model Integration, Prompt Engineering)
Job ID: 799956 - Hybrid (2 days/week onsite)
Client: Commonwealth of PA
Location: Mechanicsburg, PA 17050
Closing Date:04/16/2026
Required Experience: Seven years of relevant experience
Description

The Public Safety Delivery Center (PSDC) is seeking a Cloud Engineer to support the PSDC Solutions Management Group. This role will work primarily with the PSDC enterprise team and will focus on designing, developing, implementing, and supporting cloud-native solutions in Microsoft Azure and Amazon Web Services (AWS).
The ideal candidate will bring strong hands-on experience in cloud engineering, application modernization, workload migration, and DevOps-enabled delivery. Candidates must be able to demonstrate direct personal experience performing this work in prior projects and engagements. Experience limited to general team participation will not be sufficient.
This role also requires the ability to support applied AI solution patterns, including GPT model integration, prompt engineering, and enterprise use cases such as summarization, semantic search, and intelligent chatbot capabilities.
Role Description

  • Design, develop, implement, and support cloud-native applications, services, and APIs using technologies such as .NET, .NET Core, containers, Python, and related tools, as appropriate to the solution.
  • Design, implement, and support secure, scalable, and reliable solutions in Azure and AWS environments.
  • Deploy and manage cloud infrastructure and platform services in support of application modernization, integration, and enterprise solution delivery.
  • Support cloud migration, integration, and modernization efforts across a range of PSDC business processes.
  • Work with Azure services such as App Service, Service Bus, API Management, Logic Apps, Azure Functions, Azure SQL Managed Instance, Azure SQL Database, Blob Storage, File Storage, and Table Storage.
  • Work with Azure infrastructure and networking components such as Virtual Machines, Virtual Networks, Network Security Groups, VPN Gateway, and ExpressRoute.
  • Work with AWS services such as EC2, S3, VPC, Lambda, and RDS in support of secure and reliable cloud solutions.
  • Support containerization and orchestration using technologies such as Docker, Kubernetes, AWS ECS, Azure AKS, Azure Container Apps, and similar platforms.
  • Support CI/CD pipelines and DevOps practices using tools such as Azure DevOps, GitHub, Artifactory, and NuGet.
  • Apply software engineering, software architecture, SDLC, and data management practices throughout the delivery lifecycle.
  • Contribute to technical solution design, implementation planning, engineering analysis, and related business and technical review activities.
  • Apply knowledge of networking and infrastructure concepts, including DNS, Active Directory, firewalls, load balancers, and virtual networking.
  • Support GPT-enabled solution capabilities, including prompt engineering, intelligent chatbots, document summarization, semantic search, and related business use cases.
  • Apply awareness of token usage, cost management, performance optimization, content filtering, and compliance considerations when deploying GPT-based solutions in production cloud environments, including government and public safety contexts.
Required / Desired Skills

  • Four-year college degree or equivalent technical study
  • Azure cloud platform experience, including application, storage, and networking services
  • AWS cloud platform experience, including compute, storage, and networking services
  • Cloud-native application development and integration experience
  • CI/CD and DevOps tool experience
  • AWS Certified Solutions Architect
  • Azure Fundamentals or Azure Solution Architect certification
  • .NET, .NET Core, and C#
  • Python programming
  • AI model integration and prompt engineering
  • AI-driven application development, including chatbot, summarization, or semantic search use cases