AI Engineer

AI Engineer Better Direct

• 2.2 Tempe, AZ Job Details Full-time $60,000
• $70,000 a year 2 hours ago Qualifications TensorFlow Data transformation pipeline development NumPy Prompt engineering Databases Pandas Machine learning libraries Model evaluation Machine learning frameworks Generative AI Full Job Description About the Role We are looking for a motivated AI Engineer who is passionate about large language models (LLMs), machine learning, and applied AI systems.

This position is focused on building real-world AI systems, not just experimenting with models. You will work on projects involving open-source LLMs, retrieval-augmented generation (RAG) pipelines, vector databases, and AI-powered document processing systems. The goal is to build scalable AI workflows that solve practical problems such as knowledge retrieval, document analysis, and AI-assisted automation. This role is ideal for engineers who want hands-on experience deploying AI systems used in production-like environments. Responsibilities AI & Machine Learning Development Build and experiment with open-source LLM and SLM pipelines. Design and implement Retrieval Augmented Generation (RAG) systems. Develop AI pipelines capable of processing documents, PDFs, and structured data. Work with embedding models and vector search systems. Implement prompt engineering, model evaluation, and response optimization. Assist with fine-tuning or adapting open-source models when necessary. Data Processing & AI Pipelines Build ingestion pipelines for PDFs, documents, and datasets. Implement document chunking, embedding generation, and indexing strategies. Work with vector databases to support semantic search and retrieval. Optimize pipelines for latency, scalability, and cost efficiency. Research & Experimentation Evaluate different open-source models and architectures. Compare embedding models and retrieval methods. Test improvements in RAG performance and hallucination reduction. Explore emerging techniques in Vision Language Models (VLMs). Collaboration Work with engineers to integrate AI components into applications. Document experiments and technical findings. Participate in weekly discussions on AI architecture decisions and improvements. Required Skills & Knowledge Core AI Knowledge Strong understanding of: Machine Learning Neural Networks Deep Learning fundamentals Familiarity with: Large Language Models (LLMs) Small Language Models (SLMs) Programming Strong proficiency in Python Experience with AI/ML frameworks such as: PyTorch TensorFlow Hugging Face Transformers AI Application Frameworks Experience with: LangChain or similar orchestration frameworks Prompt engineering and AI workflow building Vector Databases Conceptual and practical understanding of any vector databases such as:

Pinecone ChromaDB Milvus Qdrant FAISS Weaviate Understanding of:

embeddings similarity search indexing strategies metadata filtering RAG Systems Ability to design or understand: Retrieval pipelines Document chunking strategies Embedding pipelines Hybrid search Context window optimization RAG evaluation methods Data Processing Experience with: PDF extraction Document parsing pipelines Data preprocessing Bonus Knowledge Vision Language Models (VLMs) Multimodal AI systems Distributed AI inference GPU inference optimization Preferred Project Experience Candidates should have completed at least 1-3 hands-on AI projects, such as: Example Project 1

• RAG Knowledge Assistant Built a chatbot that answers questions from internal documentation. Implemented document ingestion, chunking, embedding generation, and vector search. Used LangChain + vector database + open-source LLM. Example Project 2
• Document AI System Created a system that extracts structured information from PDFs. Built pipelines for PDF parsing → embeddings → AI summarization. Example Project 3
• AI Research Experiment Compared multiple embedding models and evaluated search accuracy. Benchmarked RAG response quality and hallucination rates. Example Project 4
• LLM Application Built a real-world tool using open-source models (e.

g., summarizer, Q&A system, coding assistant). Real-World Experience Candidates may also have experience such as: Contributing to open-source AI projects Participating in AI hackathons Research experience in machine learning or NLP Building production-style AI APIs Deploying models using Docker or cloud platforms Working with LLM inference servers (vLLM, TGI, Ollama, etc.) Tools & Technologies (Exposure Preferred) AI / ML PyTorch Hugging Face Transformers Sentence Transformers LLM Tools LangChain LlamaIndex Open-source LLMs (LLaMA, Mistral, etc.) Vector Databases Pinecone ChromaDB Milvus Qdrant FAISS Weaviate Data Tools Pandas NumPy Deployment Docker REST APIs / FastAPI Basic cloud exposure (AWS/GCP/Azure) What You Will Gain Hands-on experience building real AI systems Exposure to modern LLM architecture and AI infrastructure Experience with RAG pipelines and vector databases Mentorship from engineers working in applied AI Opportunity to contribute to real production-style AI tools Candidate Profile We are looking for someone who: Is curious and loves experimenting with AI systems Enjoys solving practical engineering problems Can quickly learn new frameworks and models Is comfortable reading research papers and technical documentation Has strong problem-solving and debugging skills Application Requirements Please include: Resume GitHub profile (required) Links to AI/ML projects Brief description of a RAG or LLM system you have built

Pay:

$60,000.00

• $70,000.

00 per year

Work Location:

In person