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AI Engineer


Job Location:

Dallas, TX - USA

Monthly Salary: Not provided by the employer
Posted: 30 September 2026 (Yesterday)
Application Deadline: 28 December 2026
Vacancies: 1 Vacancy

Job Summary

About the Opportunity

Our client is seeking an AI Engineer to join its growing technology organization and help develop practical business-focused artificial intelligence solutions.

This is an excellent opportunity for a mid-level AI professional who has hands-on experience developing AI machine learning and/or Generative AI applications and wants to take the next step in their career.

The successful candidate will work alongside experienced technology and business professionals to design develop test and deploy AI solutions that improve business processes enhance decision-making and create new capabilities across the organization.

This is a hands-on development role for someone who enjoys building technologynot simply researching or evaluating it.

Key Responsibilities
  • Design develop test and deploy AI and Generative AI applications.
  • Work with business and technology teams to identify opportunities where AI can deliver measurable value.
  • Develop solutions utilizing Large Language Models (LLMs) RAG APIs embeddings and AI agents.
  • Integrate AI capabilities with existing enterprise applications databases and data sources.
  • Develop and maintain data pipelines required to support AI applications.
  • Work with cloud-based AI platforms and services.
  • Evaluate different AI models and technologies based on performance cost security and business requirements.
  • Develop and refine prompts and workflows to improve AI application performance.
  • Assist with deploying AI solutions into production environments.
  • Monitor AI applications and help troubleshoot performance accuracy and reliability issues.
  • Participate in testing model evaluation and continuous improvement of AI solutions.
  • Follow established security data privacy and AI governance practices.
  • Research emerging AI technologies and make recommendations for potential applications.
  • Collaborate with software engineers data professionals IT leadership and business stakeholders.
  • Document solutions processes and technical designs.
  • Contribute to the development of the organizations broader AI capabilities and best practices.
Qualifications
Required
  • Bachelors degree in computer science Engineering Data Science or related technical field or equivalent experience.
  • 24 years of professional experience in software engineering machine learning data engineering AI development or a related field.
  • Hands-on experience developing AI/ML or Generative AI applications.
  • Strong programming skills in Python.
  • Understanding of software development APIs databases and application architecture.
  • Experience working with LLMs or Generative AI technologies.
  • Experience working with cloud technologies.
  • Ability to take a technical concept from prototype through implementation.
  • Strong analytical and problem-solving skills.
  • Excellent communication and collaboration skills.
Preferred Experience
  • RAG and retrieval-based AI applications
  • Vector databases
  • AI agents and agentic workflows
  • Azure OpenAI AWS Bedrock or Google Vertex AI
  • LangChain LlamaIndex or similar frameworks
  • PyTorch TensorFlow or other ML frameworks
  • Docker and CI/CD
  • MLOps / LLMOps
  • SQL and data engineering
  • Enterprise system integrations
  • AI security governance and responsible AI practices
What Were Looking For

The ideal candidate is a builder.

You dont need to be a research scientist or have a Ph.D. in AI. Were looking for someone who understands modern AI technologies and has demonstrated the ability to actually build and deploy solutions.

The strongest candidates will typically have:

Strong software engineering foundation hands-on AI/GenAI experience business curiosity.

We are particularly interested in candidates who have built projects or applications using LLMs and can explain:

  • What problem they were solving
  • Why they selected a particular model or architecture
  • How they handled the data
  • How they evaluated the results
  • How they moved the solution toward production
  • What they would do differently the second time