Vantage Point Consulting Inc.
GenAI Application Engineer (Python & LLM Prompt Engineering)
Vantage Point Consulting Inc., Austin, Texas, us, 78716
JOB DESCRIPTION
We are seeking a highly skilled and motivated GenAI Application Engineer to join our team in Austin, TX. The ideal candidate will have hands-on experience in building and deploying Generative AI applications using Python, with a strong foundation in LLM prompt engineering, RAG pipelines, and vector database integration. This role involves working closely with cross-functional teams to design, develop, and optimize AI-driven solutions that enhance user experience and operational efficiency.
Key Responsibilities: Design and develop GenAI-powered applications using Python and modern AI frameworks. Engineer and optimize prompts for LLMs. Implement Retrieval-Augmented Generation (RAG) pipelines aggregating data from diverse sources such as conversation histories and product documentation Integrate and manage vector databases for semantic search and context retrieval. Collaborate with product managers, data scientists, and DevOps teams to deliver scalable AI solutions. Fine-tune pre-trained LLMs for domain-specific tasks and performance improvements. Ensure robust testing, monitoring, and documentation of AI models and pipelines. Required Skills:
Proficiency in Python and experience with AI/ML libraries (e.g., LangChain, Hugging Face, PyTorch, TensorFlow). Strong understanding of LLMs, prompt engineering, and RAG architecture. Experience with vector databases Familiarity with cloud platforms
Key Responsibilities: Design and develop GenAI-powered applications using Python and modern AI frameworks. Engineer and optimize prompts for LLMs. Implement Retrieval-Augmented Generation (RAG) pipelines aggregating data from diverse sources such as conversation histories and product documentation Integrate and manage vector databases for semantic search and context retrieval. Collaborate with product managers, data scientists, and DevOps teams to deliver scalable AI solutions. Fine-tune pre-trained LLMs for domain-specific tasks and performance improvements. Ensure robust testing, monitoring, and documentation of AI models and pipelines. Required Skills:
Proficiency in Python and experience with AI/ML libraries (e.g., LangChain, Hugging Face, PyTorch, TensorFlow). Strong understanding of LLMs, prompt engineering, and RAG architecture. Experience with vector databases Familiarity with cloud platforms