Lead Intelligent Solutions Engineer

McKesson

. Design, build, and maintain AI prompts, prompt libraries, LLM-based agents, copilots, chatbots, automation scripts, and end-to-end intelligent workflows .

Posted 10/3/2026full-timeRemote • Texas • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing AI solutions, including prompt engineering and automation workflows, while ensuring compliance with governance standards and security protocols. Proven ability to lead cross-functional teams in delivering scalable, production-grade AI applications in regulated environments.

Highest-signal resume keywords

Machine Learning ExpertisePrompt EngineeringAI Solution ArchitecturePython ProgrammingMicrosoft AI Ecosystem Tools

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningPrompt EngineeringAI Solution ArchitecturePython ProgrammingJavaScript ProgrammingAPI IntegrationData TransformationDeployment PracticesOrchestration FrameworksGenerative AI

Soft Skills

Stakeholder InfluenceCollaborationMentoringProblem SolvingAdaptability

Tools & Technologies

Copilot StudioPower PlatformAzure AIAzure OpenAIWorkflow Orchestration Tools

Certifications & Qualifications

Bachelor's Degree

Industry Keywords

HealthcareLife SciencesClinical ResearchRegulated EnvironmentsCompliance Standards

Tech Stack

Tools & technologies

AzureJavaScriptPython

About the role

Key responsibilities & impact

Design, build, and maintain AI prompts, prompt libraries, LLM-based agents, copilots, chatbots, automation scripts, and end-to-end intelligent workflows
Lead rapid prototyping and experimentation and convert successful pilots into scalable, production-grade solutions
Own the day-to-day technical health of deployed solutions through monitoring, troubleshooting, performance tuning, and reliability improvements
Build and maintain integrations with enterprise platforms using APIs, services, data pipelines, and workflow orchestration tools
Serve as end-to-end AI solution architect and make architectural decisions hands-on
Define and standardize agent architectures, integration and data flows, and dev/test/prod deployment strategies
Own delivery planning, value metrics, success criteria, and timelines
Identify technical risks, trade-offs, and dependencies and drive resolution
Lead high-impact enterprise AI and automation use cases
Translate loosely defined business problems into durable, production-ready AI solutions
Develop solutions for case intake, triage, summarization, decision support, intelligent document processing, workflow automation, and embedded AI assistants
Partner with business owners to validate outputs, refine logic, and operationalize adoption
Ensure compliance with enterprise AI governance standards, ethical AI principles, and corporate policies
Design secure-by-default solutions with documentation, traceability, monitoring, and audit readiness
Mitigate risks involving data privacy, access control, model behavior, hallucinations, bias, and operational resilience
Partner with architecture, security, and data governance teams on compliant solution design
Evangelize AI and intelligent automation capabilities across the organization
Produce technical documentation, user guides, and SOPs
Lead enablement sessions, workshops, and knowledge transfer
Mentor and influence standards, patterns, and best practices across AI and automation initiatives
Collaborate with business and technology partners to improve AI-enhanced workflows

Requirements

What you’ll need

Bachelor's Degree required
At least 7 years of experience in machine learning
Healthcare, life sciences, or clinical research domain experience preferred
Experience delivering solutions in regulated, compliance-driven environments preferred
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
Experience with APIs, data transformation, version control, and deployment practices
Demonstrated experience building agentic AI solutions, including RAG architectures, orchestration frameworks, and tool-calling patterns
Experience with Microsoft AI ecosystem tools, such as Copilot Studio, Power Platform, Azure AI, or Azure OpenAI, strongly preferred
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 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

Benefits

Comp & perks

Comprehensive benefits supporting physical, mental, and financial well-being
Competitive compensation package
Annual bonus or long-term incentive opportunities may be offered
Relocation assistance is not available
Visa sponsorship is not available