Lead Intelligent Solutions Engineer - Remote US

Sarah Cannon Research Institute is an oncology research organization focused on advancing treatments and improving outcomes for cancer patients through clinical research. The Lead Intelligent Solutions Engineer designs, builds, and deploys production-grade AI and intelligent automation solutions, while owning architecture, technical quality, governance, and delivery outcomes. The role also partners with business stakeholders to drive adoption and measurable improvements across enterprise workflows.

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
• Partner closely with business owners to validate outputs, refine logic, and ensure solutions are adopted, trusted, and operationalized
• Ensure all solutions comply with enterprise AI governance standards, ethical AI principles, and corporate policies
• Design and implement secure‑by‑default solutions, including documentation, traceability, monitoring, and audit readiness
• Anticipate and mitigate risks related to data privacy, access control, model behavior, hallucinations, bias, and operational resilience
• Partner with architecture, security, and data governance teams to ensure compliant solution design, especially within regulated environments
• Evangelize AI and intelligent automation capabilities across the organization by:
  • Engaging business stakeholders
  • Translating opportunities into concrete solution concepts
  • Demonstrating value through working solutions
• Produce high‑quality technical documentation, user guides, and SOPs
• Lead hands‑on enablement sessions, workshops, and knowledge transfer to drive adoption
• Act as a technical mentor and thought leader, influencing standards, patterns, and best practices across AI and automation initiatives
• Collaborate with business and technology partners to continuously improve AI‑enhanced workflows