Full stack developer Empleos en Columbus oh

We are seeking a highly skilled and motivated AI Full Stack Software Engineer to join our innovative team at CMM. In this role, you will be instrumental in designing, developing, and deploying end-to-end AI-powered solutions that enhance our healthcare products and services. You will work across the full stack, from front-end user interfaces to back-end AI model integration and data pipelines.

Responsibilities

• Develop and deliver AI-driven enterprise quality software applications.
• Build scalable applications integrating Large Language Models and other AI tools (e.g. voice recognition, document parsing, image parsing, text to speech, RAG) into new and legacy codebases
• Design and optimize data pipelines and datasets for AI system development and testing.
• Communicate modern AI concepts (LLMs, vector databases etc.) to non-technical audiences
• Iteratively craft effective prompts to maximize LLM performance and implement prompt caching strategies
• Maintain prompt libraries & benchmark performance
• Fine-tune & serve GPT-family, Llama
• Develop AI agents Semantic Kernel
• Prototype co-pilot experiences & autonomous workflows
• Fully document all work at both the code level and application level.
• The ability to learn and adapt in a changing environment.
• Collaborate with other staff and outside stakeholders on large projects.
• Must be passionate about creating solutions and solving complex problems.

Priority will be given to candidates who reside in the Atlanta, GA, and Columbus, OH metropolitan areas.

We are unable to provide sponsorship for this role presently or in the future.

Minimum Qualifications

• Minimum 2+ years of experience in Software development.
• Minimum 2+ years of experience in developing AI-enhanced applications using LLMs, voice, image, or speech models.
• B.S. degree in Computer Science or related engineering discipline, or equivalent experience

Critical Skills

• AI System Evaluation & Optimization: Proficient in sensitivity metrics (precision, recall, F1-score, AUC-ROC) and MLOps for scalable, cost-efficient AI deployment.
• RAG Development: Skilled in building RAG systems from scratch or with LangChain/LlamaIndex, using vector databases for context-aware LLM applications.
• LLM Cost Optimization: Expert in managing LLM costs (token pricing, input/output rates) via prompt caching, batching, and quantization.
• LLM Knowledge: Deep understanding of LLM advancements (GPT-3.5 vs GPT-4 vs o1/”thinking” models) for informed model selection and tuning.
• Exceptional analytical skills to extract actionable insights from data
• Experience using AI coding tools (e.g. Github Copilot, Cursor, Codeium/Windsurf)
• Proficiency in Python and AI related python libraries such as openai, tensorflow, pytorch
• Healthcare Domain Expertise, Translate clinical/business needs into AI solutions
• Proficiency in creating Node.js web apps (e.g. Express.js)
• Experience with cloud (AWS/GCP/Azure)
• Effective communication skills to collaborate with technical, DevOps and product management teams.
• Experience with Git, Docker, and Kubernetes

We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please

Our Base Pay Range for this position

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McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application.

McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates.

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