Generative AI Engineer - Austin, TX

QTechAustin, TX
Onsite

About The Position

We are seeking a Generative AI Engineer to architect, develop, and optimize generative AI models, including large language models (LLMs), for various enterprise use cases. This role involves researching and prototyping advanced generative AI techniques, collaborating with cross-functional teams, and implementing optimization strategies. You will also be responsible for integrating models into production pipelines, evaluating LLMs, creating reusable components, and ensuring the safety and fairness of deployed applications.

Requirements

  • 7+ years of experience in AI/ML; 3+ years in building generative AI systems.
  • Hands-on experience with Hugging Face Transformers, LangChain, OpenAI APIs, and vector DBs like Pinecone or FAISS.
  • Strong Python programming, including experience with PyTorch or TensorFlow.
  • Solid understanding of agentic frameworks (CrewAI, LangGraph) and orchestration tools.
  • Prior deployment experience on cloud platforms such as Azure, AWS, or GCP.
  • Knowledge of containerization (Docker, Kubernetes) and model lifecycle management (MLflow, Weights & Biases).
  • Familiarity with responsible AI, prompt injection mitigation, and safe model usage principles.

Responsibilities

  • Architect, develop, and optimize generative AI models including large language models (LLMs) for diverse enterprise use cases.
  • Research and prototype advanced generative AI techniques (e.g., transformers, diffusion models, RAG, LoRA).
  • Collaborate with data scientists, MLOps teams, and product stakeholders to align generative AI initiatives with business objectives.
  • Implement fine-tuning, prompt engineering, and model distillation techniques for performance optimization.
  • Integrate generative models into real-time applications and large-scale production pipelines.
  • Drive the evaluation of open-source and commercial LLMs for cost, performance, and compliance.
  • Create reusable components and APIs for LLM access, tool use, agentic orchestration, and generative tasks.
  • Ensure fairness, interpretability, and safety across all deployed GenAI applications.
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