Lead Intelligent Solutions Engineer - Remote US

About the role

It’s More Than a Career, It’s a Mission. Our people are the foundation of our success. By joining our growing team at Sarah Cannon Research Institute (SCRI), a subsidiary of McKesson, you will have the opportunity to become part of one of the largest community-based cancer programs to advance oncology treatments and improve outcomes for cancer patients across the globe. We look for mission-driven candidates who have a desire to advance the fight against cancer and make a difference in the lives of patients diagnosed with cancer every day. Our Mission People who live with cancer – those who work to prevent it, fight it, and survive it – are at the heart of every decision we make. Bringing the most innovative medical minds together with the most passionate caregivers in their communities, we are transforming care and personalizing treatment. Through clinical excellence and cutting-edge research, SCRI is redefining cancer care around the world.

Responsibilities

• Independently design, build, and maintain:
  • AI prompts and prompt libraries
  • LLM based agents and copilots
  • Chatbots, automation scripts, and end to end intelligent workflows
• Lead rapid prototyping and experimentation; convert successful pilots into scalable, production grade solutions
• Own the day-to-day technical health of deployed solutions, including monitoring, troubleshooting, performance tuning, and reliability improvements
• Build and maintain robust integrations with enterprise platforms using APIs, services, data pipelines, and workflow orchestration tools
• Serve as the end-to-end AI solution architect for assigned initiatives, making architectural decisions and implementing them hands on
• Define and standardize solution patterns for:
  • Agent architectures (RAG, tool calling, multi agent orchestration)
  • Integration and data flow design
  • Environmental promotion and deployment strategies (dev/test/prod)
• Own delivery planning with clearly defined value metrics, success criteria, and timelines
• Proactively identify technical risks, trade offs, and dependencies and drive resolution
• Lead execution for high‑impact, enterprise‑level AI and automation use cases, particularly complex or manual processes with measurable value potential
• Translate loosely defined business problems into durable, production‑ready AI solutions
• Typical use cases include,