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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Analyst, AI Engineer - **Company:** Cardinal Health - **Location:** Little Rock, AR, United States - **Experience:** Expert - **Salary:** $80,500.0 - $103,410.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, BigQuery, Clinical Data Repository, Cloud Computing, Cloud Storage, Data Warehousing, Software Debugging, Python (Programming Language), Machine Learning, Tensorflow, Search Technologies, Software Systems, Web Application Frameworks, Data Logging, Google Cloud, Large Language Models, Prompt Engineering, Generative AI, Jupyter, Git, Information Technology, Google Cloud Functions, Enterprise Integration, Integration Frameworks, Virtual Agents, Cloud Integration, Programming Languages, Microservices - **Published:** July 16, 2026 - **Apply:** https://dejobs.org/x/x/00F3AA59116948E9B21658BEDB1C06ED/job/ ## About the Role * Programming Languages: Strong proficiency in Python and standard SQL. * GCP Infrastructure (Basic familiarity): Experience or projects utilizing Google Cloud Platform (e.g., Cloud Storage, Cloud Run, BigQuery). * Vertex AI Suite: Basic exposure to Vertex AI Studio, Model Garden, Gemini APIs, or Vertex AI Vector Search. * GenAI / Agentic Frameworks: Conceptual understanding of LLM prompt engineering, embeddings, and agentic workflows (experience with Python frameworks like LangChain, LangGraph, LlamaIndex, or Vertex AI Agent Builder is highly regarded). * Developer Fundamentals: Comfort with Git, VS Code/Jupyter, and writing clean, modular Python code., * Bachelor's degree in mathematics, Statistics, Engineering, Computer Science, other related field, or equivalent years of relevant work experience is preferred. * 1+ years of experience preffered * Knowledge of clinical domain and datasets is a major plus * Experience in Generative AI, RAG implementation, re-ranking, vector db, embeddings etc. is a plus * Knowledge of Machine Learning and related technologies such as Tensorflow Python, Torch, Amazon SageMaker, Jupiter Notebooks, git. * Understanding of cloud data engineering and integration concepts. * Strong mathematical and statistical skills. * Prior experience in Healthcare industry and knowledge of clinical data. * Experience with Google Cloud Platform. * Knowledge of software solutions such as data warehouses and integration platforms. * Knowledge of Agile development skills and experience., Candidates who are back-to-work, people with disabilities, without a college degree, and Veterans are encouraged to apply. ## Description As an AI Engineer, you will play a key role in building, testing, and deploying cutting-edge artificial intelligence solutions natively on Google Cloud Platform (GCP). Working closely with senior engineers, you will leverage Vertex AI to integrate Large Language Models (LLMs), build Retrieval-Augmented Generation (RAG) pipelines, and develop Agentic AI systems (equipping Gemini models with tools, API access, and reasoning loops). This role is ideal for an early-career engineer who has strong Python skills, a solid grasp of foundational cloud principles, and a passion for building next-generation action-oriented AI., * GCP AI Development: Help build and configure GenAI applications utilizing the Vertex AI SDK and the Gemini model family. * Agentic Workflows: Assist in building AI agents. This includes setting up Vertex AI Extension calls, defining function schemas for Gemini tool-use, and managing agent memory/reasoning loops. * Data & Vector Pipelines: Support the ingestion of unstructured enterprise data into Vertex AI Vector Search or BigQuery to power RAG and grounding mechanisms. * Prompt Engineering & Evaluation: Design, test, and iterate on system instructions. Use Vertex AI's evaluation tools to check for response accuracy, safety, and hallucinations. * Cloud Integration: Assist in deploying and hosting lightweight AI APIs, agent endpoints, or microservices using Cloud Run or Cloud Functions. * Observability & Debugging: Monitor and debug agent execution paths and API latency using Google Cloud Logging and Cloud Trace. * Continuous Learning: Actively research and stay up-to-date with the rapidly evolving GenAI landscape, bringing fresh ideas, open-source frameworks, and tools to the team. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Kubernetes dev is fun, but setup and ops isn't! 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