Lead Intelligent Solutions Engineer

McKesson, through its subsidiary Sarah Cannon Research Institute (SCRI), is seeking a Lead Intelligent Solutions Engineer to advance oncology treatments and improve patient outcomes. This role is responsible for delivering AI and intelligent automation solutions, overseeing the design and implementation of complex systems that enhance productivity and decision-making.

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, but are not limited to:
• LLM-based agents for case intake, triage, summarization, and decision support
• Intelligent document processing across emails, PDFs, forms, and unstructured content
• Workflow automation for finance, operations, compliance, research, or shared services
• Embedded AI assistants integrated into enterprise systems to reduce manual effort, errors, and cycle time
• 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

Requirements:

• Bachelor's Degree required, Master's Degree preferred
• Advanced knowledge of generative AI, machine learning, NLP, agentic frameworks, and AI solution architectures
• Strong understanding of AI governance, MLOps, and enterprise risk management
• Healthcare, life sciences, or clinical research technology domain knowledge
• Proven experience delivering complex, production‑grade AI and automation solutions as a hands‑on builder
• Deep proficiency in prompt engineering and applying Generative AI to enterprise workflows
• Strong programming skills (typically Python and/or JavaScript) with experience in APIs, data transformation, version control, and deployment practices
• Demonstrated experience building agentic AI solutions, including RAG architectures, orchestration frameworks, and tool‑calling patterns
• Ability to rapidly prototype while maintaining enterprise‑grade security, reliability, and governance
• Demonstrated ability to operate as a senior, end‑to‑end AI solution architect while remaining hands‑on
• Comfortable owning ambiguous, high‑visibility initiatives with minimal direction
• Strong stakeholder influence skills; able to align technical decisions with business outcomes in a matrixed environment
• Track record of translating concepts into shipped, adopted, enterprise solutions
• Ability to operate simultaneously at strategic, architectural, and engineering levels
• Ability to influence change and drive adoption across teams
• Ability to prioritize, execute, and deliver outcomes under evolving requirements
• Healthcare, life sciences, or clinical research domain experience
• Experience delivering solutions in regulated, compliance‑driven environments