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AI Engineer
Job
GDH
Phoenix, AZ (In Person)
$132,080 Salary, Full-Time
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Job Description
Role Summary Develop, implement, and scale machine learning and generative AI solutions that deliver measurable business outcomes. This role blends strong software engineering expertise with hands-on experience building applications powered by modern AI frameworks and large language models. You will take models and AI systems from prototype through production release, with a focus on reliability, performance, and operational excellence. Responsibilities Build and deploy machine learning and deep learning models using production-ready engineering practices Design and implement Retrieval-Augmented Generation (RAG) pipelines, including embeddings, chunking strategies, and reranking Integrate LLM APIs into application workflows and orchestration layers Develop and maintain AI-powered APIs using FastAPI to support scalable system access Operationalize ML pipelines, including monitoring and continuous performance evaluation in production Use vector databases and semantic search techniques to improve retrieval quality and downstream results Evaluate model and LLM performance using appropriate frameworks and testing approaches Optimize AI-driven workloads for cost and latency while maintaining quality targets Containerize services and support deployments using Docker and related best practices Collaborate with cross-functional stakeholders to translate technical decisions into business impact Qualifications Strong proficiency in Python and SQL; experience with Java or JavaScript is a plus Hands-on experience developing and deploying machine learning models, including deep learning solutions Strong understanding of model selection, evaluation methods, and performance tuning Experience building and deploying APIs with FastAPI Familiarity with containerization and deployment using Docker Experience integrating LLM APIs and using at least one orchestration framework such as LangChain or LangGraph Exposure to enterprise AI platforms such as Azure ML, Snowflake Cortex, and/or MLflow Knowledge of vector databases and semantic search techniques Practical experience implementing RAG architectures, including embeddings, chunking, and reranking Ability to communicate technical concepts clearly in a business context