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Workflow Developer Jobs in Texas (NOW HIRING)

Ultimate Knowledge

AI Workflow Engineer

Dallas, TX · On-site

Level up developer experience. Bring modern AI integrations into the daily workflow of our engineering team: coding agents, automated review, intelligent test generation, and self-service tooling ...

Ultimate Knowledge

AI Workflow Engineer

Dallas, TX · Remote

Level up developer experience. Bring modern AI integrations into the daily workflow of our engineering team: coding agents, automated review, intelligent test generation, and self-service tooling ...

Fujitsu

AI/ML ENG III

Dallas, TX · On-site

$57 - $76.50/hr

See what working at 1Finity looks like at For more information, please visit AI Automation & Intelligent Workflow Developer III Position Summary The AI Automation & Intelligent Workflow Developer III ...

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$15

$49

$76

How much do workflow developer jobs pay per hour?

As of Sep 4, 2026, the average hourly pay for workflow developer in Texas is $49.23, according to ZipRecruiter salary data. Most workers in this role earn between $37.64 and $60.24 per hour, depending on experience, location, and employer.

What is a workflow developer?

A Workflow Developer designs, builds, and optimizes automated workflows that streamline business processes. They work with tools like workflow automation platforms, low-code/no-code solutions, and scripting languages to enhance efficiency. Their responsibilities often include analyzing requirements, integrating systems, and troubleshooting workflow issues. This role requires a mix of technical skills, problem-solving abilities, and an understanding of business operations.

What does a workflow developer do?

Workflow Developers are often tasked with mapping out existing business processes, designing and implementing automated solutions, and integrating different software systems to improve efficiency. Daily responsibilities may include collaborating with business analysts and stakeholders to understand requirements, configuring workflow engines, testing automation scripts, and troubleshooting issues. Many Workflow Developers also participate in user training and documentation to ensure smooth transitions to automated processes. This dynamic role involves cross-team collaboration and continues to evolve as new technologies and business needs arise, making each project unique and rewarding.

What are the key skills and qualifications needed to thrive as a workflow developer?

To thrive as a Workflow Developer, you need strong analytical skills, experience in process automation, and a background in computer science or IT. Familiarity with workflow automation tools such as Nintex, UiPath, or Microsoft Power Automate, as well as certifications in these platforms, is highly beneficial. Creative problem-solving, attention to detail, and effective communication are key soft skills that enhance success in this role. These abilities allow Workflow Developers to streamline operations, collaborate with various stakeholders, and deliver efficient, scalable solutions to organizational processes.

What are the most commonly searched types of Workflow Developer jobs in Texas?

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Workflow Developer jobs near you
Infographic showing various Workflow Developer job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $102,393 per year, or $49.2 per hour.

AgenticAI Workflow Engineer | Onsite

Photon

Dallas, TX • On-site

Full-time

Re-posted 2 days ago

Key responsibilities

  • Develop and orchestrate AI agent workflows using LangGraph, LangChain, and multi-agent architectures.

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

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

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.

Apply