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Fastapi Jobs in Texas (NOW HIRING)

GTSS Inc

AI Engineer

Dallas, TX · On-site

Continue prioritizing candidates with experience in Python, FastAPI, RAG/GraphRAG, Docker, cloud platforms (AWS/Azure/Google Cloud Platform), and LLMOps/LLM evaluation.

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Fastapi information

Do any big companies use FastAPI?

Several large companies and organizations use FastAPI for building high-performance APIs due to its speed and ease of use with Python. Notable examples include Microsoft, Netflix, and Uber, which leverage FastAPI in various projects to improve scalability and developer productivity.

What is a FastAPI worker?

A FastAPI worker is a process that runs an instance of a FastAPI application, handling incoming HTTP requests in a production environment. It is typically managed by an ASGI server like Uvicorn or Gunicorn, which can spawn multiple workers to improve concurrency and performance. Understanding how to configure and scale FastAPI workers is important for deploying high-performance APIs.

Is Netflix using FastAPI?

There is no publicly available information confirming that Netflix uses FastAPI in its technology stack. Netflix primarily relies on Java, Python, and other frameworks for its backend services. FastAPI is a popular Python web framework known for its speed and ease of use, but its adoption by specific companies like Netflix is not publicly documented.

What are the key skills and qualifications needed to thrive in the Fastapi position, and why are they important?

To thrive as a FastAPI Developer, you need strong proficiency in Python, REST API design, and knowledge of web frameworks, ideally supported by a relevant degree or industry certifications. Familiarity with tools like Docker, Git, asynchronous programming, and testing frameworks such as Pytest is often required. Excellent problem-solving skills, adaptability, and clear communication are valuable soft skills in this role. These competencies ensure robust API development, seamless collaboration, and the delivery of high-quality software solutions in agile environments.

What are the typical daily responsibilities of a FastAPI Developer?

As a FastAPI Developer, your daily tasks typically include designing and implementing RESTful APIs, writing efficient and maintainable code, and performing thorough testing to ensure reliability and performance. You may also collaborate closely with frontend developers, DevOps engineers, and QA teams to integrate new features and troubleshoot issues. Regular code reviews, documentation, and participation in agile development meetings are common parts of the workday. This collaborative and dynamic environment allows you to make a direct impact on the product while growing your skills through hands-on problem-solving and teamwork.

What is a FastAPI job?

A FastAPI job typically involves developing, maintaining, and optimizing web applications and APIs using the FastAPI framework. FastAPI is a modern, high-performance web framework for Python that is designed for building APIs quickly with automatic OpenAPI generation. Professionals in this role are expected to have experience with Python, asynchronous programming, and API development, often working with databases, authentication, and cloud services.

Is FastAPI in demand?

FastAPI is a popular web framework for building APIs with Python, and demand for developers skilled in FastAPI is growing due to its efficiency and ease of use. Companies adopting modern, high-performance backend solutions often seek developers familiar with FastAPI, especially those with knowledge of asynchronous programming and related tools like Python and Docker.
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What are popular job titles related to Fastapi jobs in Texas? For Fastapi jobs in Texas, the most frequently searched job titles are:
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Fastapi jobs near you
Infographic showing various Fastapi job openings in Texas as of July 2026, with employment types broken down into 91% Full Time, 3% Part Time, and 6% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution.
Data Scientist - Gen AI ML - Tampa/Irving/ Mississauga

Data Scientist - Gen AI ML - Tampa/Irving/ Mississauga

Photon

Irving, TX • On-site

Other

Medical, Dental, Vision, Retirement, PTO

Re-posted 19 days ago

Job description

Role Summary:
We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices.

Technical Stack:

LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment.

Frameworks: LangChain, LlamaIndex, and Hugging Face.

Orchestration: LangGraph and Multi-Agent Systems (MAS).

Development: Python, FastAPI, and Asynchronous Programming.

RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies.

ML/DL: PyTorch, TensorFlow, and Model Fine-tuning.

Deployment: Docker, Production API management, and LLM monitoring.

Tools: Prompt Engineering, Workflow Design, and GenAI Optimization.

Key Responsibilities:

Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures.

Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval.

Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements.

Design and deploy high-performance, scalable backend services using FastAPI and Async Python.

Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance.

Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools.

Containerize and deploy AI services via Docker to production environments.

Required Qualifications:

5 years of hands-on experience building and deploying GenAI applications in a production setting.

Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.).

Experience with vector search, embedding models, and advanced data retrieval patterns.

Knowledge of model fine-tuning techniques and local LLM quantization/hosting.

Familiarity with production-grade monitoring, API security, and CI/CD for ML.

Compensation, Benefits and Duration

Minimum Compensation: USD 48,000
Maximum Compensation: USD 170,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post

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