1

Chat Gpt Operator Jobs (NOW HIRING)

Senior Financial Analyst

Los Angeles, CA ยท Remote

$120K - $150K/yr

Translate on-chain data alongside traditional financials into insights that drive operating ... GPT, Gemini) to automate or accelerate financial analysis workflows, not just as a chat interface ...

OR

$466K - $750K/yr

... GPT, and Gemini), the Assistance API for conversational use cases, the MCP Gateway that connects ... operating, AND evaluating LLM agents in production - not just chat-completion apps or prototypes.

Showing results 41-49

Chat Gpt Operator information

See salary details

$12

$20

$29

How much do chat gpt operator jobs pay per hour?

As of Aug 7, 2026, the average hourly pay for chat gpt operator in the United States is $20.34, according to ZipRecruiter salary data. Most workers in this role earn between $17.07 and $22.84 per hour, depending on experience, location, and employer.

How do I become a Chat Gpt Operator?

To become a Chat GPT Operator, you typically need strong communication skills, familiarity with AI and chatbot platforms, and the ability to monitor and manage AI interactions. Some roles may require basic technical knowledge or experience with customer service tools. Gaining relevant experience and understanding AI guidelines can improve your chances of securing such a position.

What is the difference between Chat Gpt Operator vs Content Writer?

AspectChat Gpt OperatorContent Writer
Required SkillsPrompt engineering, AI tool familiarity, basic editingWriting, editing, research skills
Work EnvironmentRemote, tech-focused, AI platformsRemote or on-site, media or marketing firms
Industry UsageAI, tech, customer supportMedia, marketing, publishing

While both roles involve content creation, a Chat Gpt Operator specializes in managing AI prompts and optimizing AI-generated outputs, whereas a Content Writer focuses on producing original written content. The Chat Gpt Operator works closely with AI tools and requires familiarity with prompt engineering, while the Content Writer emphasizes research and storytelling skills. Both roles are essential in digital content workflows but serve different functions within the content creation process.

How to get a job with Chat GPT Operator?

To become a Chat GPT Operator, candidates typically need strong communication skills, familiarity with AI and chatbot platforms, and the ability to monitor and manage AI interactions. Relevant experience in customer service, data annotation, or technical support can improve chances, and some roles may require basic knowledge of programming or AI tools. Applying through job boards or company career pages and demonstrating proficiency in managing AI conversations are common steps.

What does the Chat GPT operator do?

A Chat GPT operator manages and monitors AI language models like ChatGPT, ensuring they generate accurate and appropriate responses. They may also troubleshoot issues, fine-tune the model, and use tools or prompts to improve performance, often working in environments that require strong communication and technical skills.
More about Chat Gpt Operator jobs
What cities are hiring for Chat Gpt Operator jobs? Cities with the most Chat Gpt Operator job openings:
What states have the most Chat Gpt Operator jobs? States with the most job openings for Chat Gpt Operator jobs include:
What job categories do people searching Chat Gpt Operator jobs look for? The top searched job categories for Chat Gpt Operator jobs are:
Infographic showing various Chat Gpt Operator job openings in the United States as of August 2026, with employment types broken down into 40% Full Time, 58% Part Time, 1% Contract, and 1% Nights. Highlights an 99% Physical, and 1% Remote job distribution, with an average salary of $42,306 per year, or $20.3 per hour.

Senior Data and AI Engineer (Insurance Domain)

Accord Technologies Inc.

Philadelphia, PA โ€ข On-site

$115K - $138K/yr

Contractor

Re-posted 23 days ago


Job description

Senior Data and AI Engineer (Insurance Domain)
Location:  Philadelphia, PA
Position type: Onsite role (need NJ, PA based candidates who can join immediately)
Tax type: W2 contract

Candidate should be available to start by next week.

Job Description:
The role owns the full technical stack from the architecture slide: connectors and ingestion framework, OneLake Medallion staging, GraphDB triple store, Vector Index, Agentic RAG orchestrator, LLM gateway, guardrails, and the consumption UI with conversational chat, SPARQL trace explainability, and graph explorer.

Knowledge Graph & Semantic Technologies (Must-Have)

•            3+ years hands-on experience with graph databases (GraphDB, Neo4j, Stardog)in a production or advanced PoC context

•            Working proficiency with semantic web standards

•            Experience loading, validating, and querying ontologies in a triple store environment

•            Familiarity with ontology authoring tools (Protégé, Metaphactory) sufficient to collaborate with the Data Consultant on model iterations

AI / ML Engineering & LLM Integration (Must-Have)

•            Demonstrated experience building RAG (Retrieval-Augmented Generation) pipelines, ideally with agentic orchestration patterns

•            Hands-on experience with vector databases (Azure AI Search, pgvector, Pinecone, Weaviate, or Qdrant) for embedding and retrieval

•            Experience integrating LLM APIs (Anthropic Claude, OpenAI GPT, or Azure OpenAI) with prompt engineering, guardrails, and citation enforcement

•            Familiarity with NL-to-SPARQL or NL-to-SQL generation techniques, including few-shot prompting and schema-grounding approaches

•            Understanding of AI safety guardrails: prompt injection defense, output sandboxing, and confidence scoring

Delivery & Collaboration (Must-Have)

•            Comfortable operating in an accelerated 8-week delivery timeline with weekly milestone gates and hard dependencies

•            Ability to work closely with a Data Modeller/Ontologist to translate conceptual models into working technical implementations

•            Experience in financial services or insurance data environments is preferred but not required, provided strong technical depth in the above areas

Data Engineering & Microsoft Fabric (Good to-Have)

•            Strong Python engineering skills with experience building data pipelines, ETL/ELT processes, and metadata ingestion frameworks

•            Experience with Microsoft Fabric ecosystem: OneLake, Lakehouse, Notebooks, Data Factory / pipelines, and Medallion architecture (Bronze/Silver/Gold)

•            Familiarity with JDBC/ODBC connectors, REST API integration, and file parsing (Excel, CSV, JSON) for metadata extraction

•            Experience with Trino, Databricks SQL, or equivalent federated query engines