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Prompt Engineering Jobs in Texas (NOW HIRING)

Photon

AgenticAI Workflow Engineer | Onsite

Dallas, TX · On-site

Prompt Engineering, AI Workflow Design, LLM Evaluation, Agent Monitoring, GenAI Optimization. Key Responsibilities: * Develop and orchestrate AI agent workflows using LangGraph, LangChain, and multi ...

Northern Base

Gen AI Engineer

Austin, TX · On-site

Prompt Engineering & LLM Orchestration * LLM Evaluation Frameworks & Responsible AI Practices * REST API Development & Automated API Testing * Java/Kotlin Development * CI/CD using Maven & Jenkins

Addison Group

AI Developer

The Woodlands, TX · On-site

$100K - $120K/yr

Agentic AI: Experience building and orchestrating single‑agent and multi‑agent systems, including prompt engineering, RAG pipelines, and agent decision logic. * Integration Development:

Cliff Services Inc

Python / GenAI Developer

Dallas, TX · On-site

$50 - $68.75/hr

Data Ingestion: Assist in building data pipelines, prompt engineering, and parsing banking documents. * Testing: Write unit tests to validate the accuracy and safety of AI model outputs. Required ...

cyberThink, Inc.

AI DevOps Developer

Plano, TX · On-site

Implement and maintain RAG pipelines, prompt engineering, agentic workflows, tool/function calling, and LLM integrations. Provide ongoing DevOps and production support, including monitoring ...

Photon

AgenticAI Workflow Engineer | Onsite

Dallas, TX · On-site

Prompt Engineering, AI Workflow Design, LLM Evaluation, Agent Monitoring, GenAI Optimization. Key Responsibilities: * Develop and orchestrate AI agent workflows using LangGraph, LangChain, and multi ...

Select Minds LLC

Agentic AI Engineer

Dallas, TX · On-site

This role requires a strong software engineering background with practical experience in AI orchestration, machine learning integration, prompt engineering, and LLMOps. Responsibilities * Design ...

YO AI Labs

Mental Health Expert - Remote

Austin, TX · Remote

$200 - $350/hr

You will work with LLMs, RAG, prompt engineering, multi-agent systems, and cloud AI platforms to develop production-grade machine learning applications. Key Responsibilities * Design, implement, and ...

Marlabs LLC

Sr. AI DevOps Developer

Plano, TX · On-site

Implement and maintain RAG pipelines, prompt engineering, agentic workflows, tool/function calling, and LLM integrations. * Provide ongoing DevOps and production support, including monitoring ...

YO AI Labs

Mental Health Expert - Remote

Dallas, TX · Remote

$200 - $350/hr

You will work with LLMs, RAG, prompt engineering, multi-agent systems, and cloud AI platforms to develop production-grade machine learning applications. Key Responsibilities * Design, implement, and ...

Jobtailor

AI Engineer, Advanced English

Houston, TX · On-site

Proficiency in prompt design and evaluation (prompt engineering) * Google Agent Development Kit (ADK) - Expert * Python - Expert * English - C1 * Ability to work independently in fast-paced ...

YO AI Labs

Mental Health Expert - Remote

Houston, TX · Remote

$200 - $350/hr

You will work with LLMs, RAG, prompt engineering, multi-agent systems, and cloud AI platforms to develop production-grade machine learning applications. Key Responsibilities * Design, implement, and ...

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All JobsPrompt Engineering JobsPrompt Engineering Jobs in Texas

Prompt Engineering information

See Texas salary details

$30.3K

$58.7K

$89K

How much do prompt engineering jobs pay per year?

As of Sep 14, 2026, the average yearly pay for prompt engineering in Texas is $58,673.00, according to ZipRecruiter salary data. Most workers in this role earn between $43,800.00 and $67,100.00 per year, depending on experience, location, and employer.

What is prompt engineering?

A Prompt Engineering job involves designing, refining, and optimizing prompts to improve the performance of AI language models. Prompt engineers work with large language models (LLMs) to generate accurate, relevant, and high-quality responses. They experiment with different phrasing techniques, fine-tune AI outputs, and collaborate with developers to enhance model capabilities. This role is essential in ensuring AI systems provide reliable and useful responses for various applications.

What skills and qualifications are needed for prompt engineering?

To excel in Prompt Engineering, a strong grasp of natural language processing (NLP), machine learning concepts, and analytical thinking is essential, often supported by a degree in computer science or a related field. Familiarity with AI platforms, code repositories (such as GitHub), and prompt development tools is typically required. Excellent problem-solving, creativity, and cross-functional communication skills help Prompt Engineers effectively collaborate and refine model outputs. These capabilities enable the creation of precise, effective prompts driving high-quality AI responses in rapidly evolving technical environments.

What are the most common challenges faced by prompt engineers in their daily work?

Prompt Engineers frequently encounter challenges such as ensuring the clarity and relevance of prompts to achieve accurate AI responses, troubleshooting inconsistent model behavior, and staying updated with evolving AI technologies. Balancing experimentation with efficiency is often essential, as iterative testing and refinement are core parts of the workflow. Collaboration with data scientists, product managers, and other engineers is common, requiring adaptability and strong communication skills. These challenges make the role dynamic and rewarding for professionals who enjoy problem-solving and innovation.

Is prompt engineering still in demand?

Prompt engineering is currently in high demand as organizations seek experts to optimize interactions with AI language models. The role requires skills in natural language processing, prompt design, and familiarity with AI tools, making it a valuable specialization in AI development and deployment.

What do you do as a prompt engineer?

A prompt engineer designs and refines prompts to optimize the performance of AI language models. They analyze model responses, experiment with prompt structures, and use tools like AI development platforms to improve accuracy and relevance in outputs.

What are the most commonly searched types of Prompt Engineering jobs in Texas?

The most popular types of Prompt Engineering jobs in Texas are:

What are popular job titles related to Prompt Engineering jobs in Texas?

For Prompt Engineering jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Prompt Engineering jobs in Texas look for?

The top searched job categories for Prompt Engineering jobs in Texas are:

What cities in Texas are hiring for Prompt Engineering jobs?

Cities in Texas with the most Prompt Engineering job openings:

Prompt Engineering jobs near you
Infographic showing various Prompt Engineering job openings in Texas as of September 2026, with employment types broken down into 1% Internship, 87% Full Time, 7% Part Time, 1% Temporary, 3% Contract, and 1% Nights. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $58,673 per year, or $28.2 per hour.

AgenticAI Workflow Engineer | Onsite

Dallas, TX • On-site

Photon
IT Services • 1 - 10 employees
Learn more about Photonexternal

Full-time

Re-posted 12 days ago

Job description


Agentic AI Workflow Engineer
We are seeking an Agentic AI Workflow Engineer to design, build, and optimize intelligent AI-driven workflows using Large Language Models (LLMs), AI agents, and enterprise automation frameworks. You will develop agentic applications that can reason, retrieve knowledge, interact with enterprise systems, and automate complex business processes.
The ideal candidate combines strong software engineering fundamentals with hands-on experience in Generative AI application development, agent orchestration, RAG pipelines, prompt engineering, and API integrations.
Technical Stack:
LLMs:
OpenAI GPT, Claude, Gemini, Llama, Mistral, and other open-source LLMs.
Agent Frameworks:
LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen.
Agentic AI Concepts:
Multi-Agent Systems (MAS), Agent Planning, Tool Calling, Memory Management, Human-in-the-Loop (HITL) workflows.
Development:
Python, FastAPI, REST APIs, Async Programming.
RAG & Knowledge Engineering:
Vector Databases, PostgreSQL, pgvector, Redis Vector Search, Elasticsearch, Embeddings, Semantic Search, Retrieval Optimization.
Workflow Orchestration:
LangGraph workflows, Agent State Management, Workflow Automation, Event-driven workflows.
Cloud & Deployment:
AWS/Azure/GCP, Docker, CI/CD pipelines, API deployment.
Tools:
Prompt Engineering, AI Workflow Design, LLM Evaluation, Agent Monitoring, GenAI Optimization.
Key Responsibilities:
  • Develop and orchestrate AI agent workflows using LangGraph, LangChain, and multi-agent architectures.

  • Design agent behaviors including:

  • Goals and instructions

  • Tool usage

  • Reasoning flows

  • Memory management

  • Error handling and recovery

  • Build RAG-based AI applications by integrating enterprise knowledge sources, vector databases, and embedding models.

  • Develop AI agents capable of interacting with enterprise systems through APIs, databases, and external tools.

  • Implement function calling and tool integrations enabling agents to perform real-world actions.

  • Create reusable agent components, workflow templates, and AI automation patterns.

  • Develop backend services and APIs using Python, FastAPI, and asynchronous programming.

  • Optimize prompts, agent workflows, and retrieval strategies to improve:

  • Accuracy

  • Response quality

  • Latency

  • Cost efficiency

  • Implement Human-in-the-Loop workflows for approval-based enterprise processes.

  • Build evaluation pipelines to measure agent performance, hallucination rates, and task completion accuracy.

  • Deploy and monitor GenAI applications using cloud platforms, containerization, and observability tools.

  • Collaborate with AI architects, product managers, and domain teams to convert business processes into agentic AI solutions.

Required Qualifications:
  • 3-6 years of experience in software engineering, AI engineering, or Generative AI application development.

  • Hands-on experience building LLM-powered applications using Python.

  • Strong understanding of:

  • LLM concepts

  • Prompt engineering

  • RAG architecture

  • AI agent workflows

  • Vector search concepts

  • Experience with agent frameworks such as:

  • LangGraph

  • LangChain

  • LlamaIndex

  • Semantic Kernel

  • CrewAI

  • Experience integrating LLM applications with REST APIs, databases, and enterprise systems.

  • Knowledge of vector databases, embeddings, semantic search, and retrieval optimization techniques.

  • Experience developing production-quality Python applications using FastAPI or similar frameworks.

  • Familiarity with Docker, cloud deployment, CI/CD practices, and API security.

  • Understanding of AI evaluation techniques including:

  • Response quality assessment

  • Prompt testing

  • Agent workflow validation

  • Exposure to AI governance concepts:

  • Responsible AI

  • Guardrails

  • Data privacy

  • Prompt injection prevention

Preferred Qualifications:
  • Experience building autonomous AI agents or multi-agent workflows.

  • Experience with enterprise automation, IT operations, customer service, or business process automation use cases.

  • Experience with observability platforms for monitoring AI applications.

  • Contributions to open-source AI frameworks or GenAI projects.

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