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Generative Ai Researcher Jobs in Texas (NOW HIRING)

PB consulting

Generative AI Engineer

Irving, TX · On-site

Collaborate with engineers, researchers, product managers, and business stakeholders to translate ... Build and deploy Generative AI solutions using Large Language Models (LLMs), text generation, and ...

New

PB consulting

Generative AI Engineer

Grapevine, TX

Collaborate with engineers, researchers, product managers, and business stakeholders to translate ... Build and deploy Generative AI solutions using Large Language Models (LLMs), text generation, and ...

New

PB consulting

Generative AI Engineer

Grand Prairie, TX · On-site

Collaborate with engineers, researchers, product managers, and business stakeholders to translate ... Build and deploy Generative AI solutions using Large Language Models (LLMs), text generation, and ...

New

US Bank

AI Research Scientist

Irving, TX · On-site

Key Responsibilities AI Research & Innovation * Monitor, evaluate, and experiment with emerging AI ... Generative AI & Agentic Systems * Develop solutions leveraging foundation models, generative AI ...

Stantec

Generative AI Developer

Austin, TX · On-site

... research, tools, and frameworks in Generative AI, Agentic AI, and multi-agent systems. • Design, build, and deploy agentic solutions using Microsoft Copilot Studio, including configuration of ...

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Generative Ai Researcher information

What are some common challenges generative AI researchers face when transitioning models from research to production environments?

Generative AI Researchers often encounter challenges when moving models from experimental research settings into real-world production. These challenges include ensuring models are robust to diverse, unseen data, optimizing for computational efficiency, and addressing potential biases or ethical concerns present in generated outputs. Collaboration with engineering teams is key to deploying scalable solutions, while ongoing monitoring is necessary to maintain model performance and compliance. Researchers should be prepared to iterate on their models post-deployment based on feedback and real-world results.

What does a generative AI researcher do?

A Generative AI Researcher studies and develops artificial intelligence models that can create new content such as text, images, music, or code. They work on advancing algorithms like generative adversarial networks (GANs), variational autoencoders (VAEs), and large language models to improve their performance and applications. Their work often involves designing experiments, analyzing data, publishing research, and collaborating with other scientists and engineers to push the boundaries of AI creativity and utility.

What is the difference between Generative Ai Researcher vs Machine Learning Engineer?

AspectGenerative Ai ResearcherMachine Learning Engineer
CredentialsAdvanced degrees in AI, Computer Science, or related fields; research experienceDegree in Computer Science, Data Science, or related fields; coding skills
Work EnvironmentResearch labs, academia, R&D departmentsTech companies, startups, product teams
Industry UsageFocus on developing generative models like GANs, VAEs, transformersImplementing ML models for various applications, including generative tasks

While both roles involve AI and machine learning, Generative Ai Researchers primarily focus on developing new generative models and advancing AI research, often working in academic or research settings. Machine Learning Engineers typically implement and deploy ML models in production environments across industries. The roles overlap in skills and tools but differ in their core focus and work environment.

What are the key skills and qualifications needed to thrive as a generative AI researcher, and why are they important?

To thrive as a Generative AI Researcher, you need a strong background in computer science, mathematics, and machine learning, typically supported by an advanced degree (Master's or PhD) in a relevant field. Proficiency in programming languages such as Python, experience with deep learning frameworks like TensorFlow or PyTorch, and familiarity with research tools and publication processes are essential. Creative problem-solving, critical thinking, and effective collaboration skills help researchers innovate and communicate complex ideas. These skills and qualities are crucial for advancing AI technologies, publishing impactful research, and driving progress in this rapidly evolving field.
What are popular job titles related to Generative Ai Researcher jobs in Texas? For Generative Ai Researcher jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Generative Ai Researcher jobs in Texas look for? The top searched job categories for Generative Ai Researcher jobs in Texas are:
What cities in Texas are hiring for Generative Ai Researcher jobs? Cities in Texas with the most Generative Ai Researcher job openings:
Generative Ai Researcher jobs near you
Infographic showing various Generative Ai Researcher job openings in Texas as of August 2026, with employment types broken down into 80% Full Time, 17% Part Time, and 3% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution.

Generative AI Engineer

Lares IT Solutions Inc

Plano, TX • On-site

Other

Posted 18 days ago

Job description

Generative AI Developer

Plano, TX

We are seeking a seasoned Generative AI Developer with expertise in AWS Bedrock and enterprise-grade application development. This role combines advanced AI/ML engineering with the ability to design, build, and deliver scalable, intelligent systems. The ideal candidate will have a proven track record in deploying generative AI solutions, architecting microservices, and integrating AI/LLM capabilities into modern enterprise applications.

Key Responsibilities

  • AI System Design & Implementation: Architect and develop scalable AI agent frameworks tailored to business needs, leveraging foundational models and AWS Bedrock.
  • Bedrock Model Expertise: Fine-tune and deploy Bedrock models (LLMs, multimodal models) for use cases such as vector-based retrieval and Retrieval-Augmented Generation (RAG).
  • Enterprise Application Development: Design, develop, and deliver multi-tier, enterprise-grade applications with a strong focus on microservices, REST APIs, and Kafka-based integrations.
  • Optimization & Reliability: Enhance model performance while balancing computational cost, ensuring deployed AI systems are robust, accurate, and maintainable.
  • Innovation & Integration: Actively integrate AI/ML capabilities and LLM-based tooling into modern software systems, staying current on emerging technologies.
  • Collaboration: Work closely with data scientists, engineers, product managers, and stakeholders to translate AI research into production-ready systems.
  • Leadership & Best Practices: Mentor teams, drive engineering excellence through TDD, BDD, and DoD, and ensure delivery of high-quality software solutions.
  • Data & Infrastructure: Manage large datasets, model lifecycle tools (MLflow, Kubeflow), and containerization (Docker, Kubernetes) for reproducible, scalable environments.

Required Skills & Experience

Technical Skills

  • AI/ML & LLM: AWS Bedrock, OpenAI GPT-4 API, LangChain, Hugging Face Transformers, RAG pipelines, LLM orchestration, prompt engineering, AI-assisted coding tools (GitHub Copilot)
  • Languages: Python (scripting & AI automation), Java (working knowledge)
  • Frameworks: Spring Boot, Spring AI, Microservices, Apache Camel, Fuse ESB
  • Messaging: Apache Kafka, ActiveMQ Artemis, event-driven architecture
  • Frontend: React, HTML5, CSS3, AJAX, jQuery
  • Cloud & DevOps: AWS (EC2, S3, Lambda, SageMaker), Docker, Kubernetes, Jenkins, Ansible, Git
  • Databases: Oracle (9i/10g/12c), vector databases (Pinecone, others)
  • Testing & Tools: Cucumber (BDD), SonarQube, Postman, JIRA, Maven, Gradle, Apache Spark
  • Operating Systems: Windows, UNIX, IBM AIX

Experience: Several years of experience in AI/ML engineering, with cloud-native, generative AI integrations and familiarity with enterprise application development practices.

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