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Gpt Engineer Jobs (NOW HIRING)

CYNET SYSTEMS

Prompt Engineering Specialist

Hartford, CT · On-site

$55 - $60/hr

Pay Range: $55/hr - $60/hr Requirement/Must Have: * 1+ years of hands-on experience with LLMs (GPT ... Prompt engineering. * Snowflake Cortex. * Healthcare domain knowledge. * LLMs. * GPT. * Prompt ...

Tata Consultancy Service Limited

Engineer

Owings Mills, MD · On-site

$80K - $90K/yr

... such as GPT, VAE, and GANs. 2.Proficient in Python and have experience with machine learning ... Knowledge of data structures, algorithms, and software engineering principles. 4.Familiar with ...

Vy Systems

Fullstack AI Engineer

Washington, DC · On-site

Proven expertise (5+ years) in full stack AI engineering, including design, development, and ... Hands-on experience with LLMs (e.g., GPT-based models), GenAI frameworks, or Copilot tools for AI ...

BIGDATAAPPTECH LLC

AI Engineer

Irving, TX · On-site

We are seeking a talented AI Engineer with hands-on experience building Generative AI applications ... Hands-on experience with LLMs such as GPT, Claude, Gemini, Llama, or similar models. Experience ...

Suffolk Construction

AI Engineer

Boston, MA

Design and deploy multi-model agents that dynamically select between LLMs (Claude, GPT, Llama ... Data Engineering & Integration * Partner with Data Engineering to design robust ETL/ELT pipelines ...

Suffolk Construction

AI Engineer

Boston, MA · On-site

$107K - $222K/yr

Design and deploy multi-model agents that dynamically select between LLMs (Claude, GPT, Llama ... Data Engineering & Integration * Partner with Data Engineering to design robust ETL/ELT pipelines ...

Gainwell Technologies LLC

Expert AI Engineer

Dallas, TX · On-site

$147K - $210K/yr

Experience developing, deploying and finetuning LLMs (GPT, Gemini, Claude or similar) for real world applications including prompt engineering, model optimization and inference efficiency. * Strong ...

Gainwell Technologies LLC

Expert AI Engineer

Houston, TX · On-site

$147K - $210K/yr

Experience developing, deploying and finetuning LLMs (GPT, Gemini, Claude or similar) for real world applications including prompt engineering, model optimization and inference efficiency. * Strong ...

Straia, Inc

Senior Backend Engineer

San Francisco, CA · On-site

$200K - $225K/yr

The Role As a Senior Engineer at Straia, you'll work directly with a small team including directly ... GPT, Gemini, Claude via API * Cloud & Infra: Hosted on GCP Why Join Straia * Be one of the first ...

Gainwell Technologies LLC

Expert AI Engineer

Austin, TX · On-site

$147K - $210K/yr

Experience developing, deploying and finetuning LLMs (GPT, Gemini, Claude or similar) for real world applications including prompt engineering, model optimization and inference efficiency. * Strong ...

Sarian, Inc.

AI Chatbot Developer

Pennington, NJ · On-site

Strong experience with OpenAI's ChatGPT or GPT APIs. * Skilled in prompt engineering and managing conversational context. * Coding Assistants: Practical use of GitHub Copilot to accelerate ...

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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.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as an AI researcher, machine learning engineer, or AI director, often requiring advanced skills in programming, data analysis, and deep learning. These roles usually involve leadership, innovation, and significant responsibility, and they may be found in large tech companies or specialized AI firms with competitive compensation packages. Such salaries are often associated with seniority, expertise, and the ability to lead complex projects.

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.

What engineer makes $500,000 a year?

Senior software engineers, especially those working in high-demand fields like artificial intelligence, machine learning, or at major tech companies, can earn $500,000 or more annually through base salary, bonuses, and stock options. Achieving this level typically requires extensive experience, advanced skills, and often leadership roles or specialized expertise in cutting-edge technologies.

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 does gpt engineer do?

A GPT engineer designs, develops, and fine-tunes AI language models based on the GPT architecture. They work with large datasets, implement model training, optimize performance, and may use tools like Python and machine learning frameworks to create applications such as chatbots or content generators.

How much do GPT prompt engineers make?

GPT prompt engineers typically earn between $70,000 and $150,000 annually, depending on experience, location, and the complexity of tasks. Senior roles or those with specialized skills in AI and machine learning can command higher salaries, especially in tech hubs or companies focusing heavily on AI development.
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Gpt Engineer jobs near you
Infographic showing various Gpt Engineer job openings in the United States as of July 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution.
Machine Learning Engineer - LLMs and Agentic

Machine Learning Engineer - LLMs and Agentic

Oversight Systems Inc

Atlanta, GA • On-site

Full-time

Re-posted 29 days ago

Job description

About Oversight

Oversight is the world’s leading provider of AI-based spend management and risk mitigation solutions for large enterprises. Based in Atlanta, GA, Oversight works with many of the world’s most innovative companies and government agencies to digitally transform their spend audit and financial control processes.

Oversight’s AI-powered platform works across our customers’ financial systems to continuously monitor and analyze all spend transactions for fraud, waste, and misuse. With a consolidated, consistent view of risk across their enterprise, customers can prevent financial loss and optimize spend while strengthening the controls that improve compliance. Learn More.

Position Overview:

We are seeking a skilled and forward-looking ML Engineer with experience in Large Language Models (LLMs), generative AI, and agentic architectures to join our growing R&D and Applied AI team. This role is critical in helping Oversight deliver the next generation of agentic AI systems for enterprise spend management and risk controls.

The ideal candidate has a strong foundation in machine learning, modern deep learning frameworks, and data pipelines, coupled with hands-on experience experimenting with LLMs, small language models (SLMs), multi-agent frameworks, and retrieval-augmented generation (RAG).

You will work closely with AI/ML researchers, data engineers, and product teams to design, implement, and optimize models that power autonomous exception resolution, anomaly detection, and explainable insights. This is a hands-on engineering role where you will not only build and scale ML systems but also actively contribute to cutting-edge applied research in agentic AI.

Core ML/LLM Engineering
  • Contribute to the design, training, fine-tuning, and deployment of ML/LLM models for production.
  • Implement RAG pipelines using vector databases.
  • Work with frameworks like LangChain, LangGraph, MCP to prototype and optimize multi-agent workflows.
  • Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions.
  • Integrate memory, evidence packs, and explainability modules into agentic pipelines.
  • Work hands-on with multiple LLM ecosystems:
    • OpenAI GPT models (GPT-4, GPT-4o, fine-tuned GPTs).
    • Anthropic Claude (Claude 2/3 for reasoning and safety-aligned workflows).
    • Google Gemini (multimodal reasoning, advanced RAG integration).
    • Meta LLaMA (fine-tuned/custom models for domain-specific tasks).
Data & Infrastructure
  • Collaborate with Data Engineering to build and maintain real-time and batch data pipelines that serve ML/LLM workloads.
  • Conduct feature engineering, preprocessing, and embeddings generation for structured and unstructured data.
  • Implement model monitoring, drift detection, and retraining pipelines.
  • Leverage cloud ML platforms (AWS Sagemaker, Databricks ML) for experimentation and scaling.
Research & Applied Innovation
  • Explore and evaluate emerging LLM/SLM architectures and agent orchestration patterns.
  • Experiment with generative AI and multimodal models to extend capabilities beyond text (images, structured financial data).
  • Collaborate with R&D to prototype autonomous resolution agents, anomaly detection models, and reasoning engines.
  • Translate research prototypes into production-ready components.
Collaboration & Delivery
  • Work cross-functionally with R&D, Data Science, Product, and Engineering to deliver business-aligned AI features.
  • Participate in design reviews, architecture discussions, and model evaluations.
  • Document processes, experiments, and results effectively for knowledge sharing.
  • Mentor junior engineers and contribute to ML engineering best practices.


Education, Experience and Skills

Required

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.
  • 3+ years of experience building and deploying ML systems.
  • Proficiency in Python and libraries such as PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.
  • Hands-on experience with LLMs/SLMs (fine-tuning, prompt design, inference optimization).
  • Demonstrated experience with at least two of the following ecosystems:
    1. OpenAI GPT models (chat, assistants, fine-tuning).
    2. Anthropic Claude (safety-first AI for reasoning and summarization).
    3. Google Gemini (multimodal reasoning, enterprise-scale APIs).
    4. Meta LLaMA (open-source, fine-tuned models).
  • Familiarity with vector databases, embeddings, and RAG pipelines.
  • Ability to work with structured and unstructured data at scale.
  • Knowledge of SQL and distributed data frameworks (Spark, Ray).
  • Strong understanding of ML lifecycle: data prep, training, evaluation, deployment, monitoring.
Preferred Qualifications
  • Experience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).
  • Knowledge of AI safety, guardrails, and explainability techniques.
  • Hands-on experience deploying ML/LLM solutions in cloud environments (AWS, GCP, Azure).
  • Experience with CI/CD for ML (MLOps), monitoring, and observability.
  • Familiarity with anomaly detection, fraud/risk modeling, or behavioral analytics.
  • Contributions to open-source AI/ML projects or publications in applied ML research.