Forward Deployed Engineer, MT Labs

McKesson•Irving, TX

About The Position

McKesson Technology Labs is seeking a Forward Deployed Engineer who pairs deep software engineering expertise with curiosity about real-world workflows. The engineer will work with McKesson teams to identify high-value problems and turn ambiguous needs into secure, production-ready solutions. This is a hands-on senior engineering role involving the design, coding, testing, deployment, and operation of full-stack products, which may include user interfaces, APIs, services, data pipelines, enterprise integrations, and applied AI capabilities. The role also provides technical direction, mentoring, and delivery-pod leadership when needed. The engineer will help determine whether to use an existing product, combine available capabilities, or build a new solution by evaluating quality, risk, cost, and measurable outcomes. Solutions should be designed for security, privacy, compliance, monitoring, observability, incident response, and long-term operational support.

Requirements

10 or more years of relevant professional experience in software engineering, automation engineering, solutions engineering, or a related technical field; less experience may be considered with relevant advanced education.
Demonstrated experience designing, coding, deploying, and supporting production software solutions.
Full-stack engineering experience across user interfaces, backend services, APIs, data processing, and system integrations.
Experience leading technical design or architecture for complex, cross-functional initiatives.
Experience with cloud platforms, CI/CD, version control, automated testing, and production observability.
Ability to translate unclear business requirements into practical technical solutions.
Experience incorporating security, privacy, reliability, and compliance requirements into software delivery.
Bachelor’s degree in computer science, engineering, information systems, or a related field, or an equivalent combination of education and professional experience.

Nice To Haves

Experience building AI-enabled products using LLMs, retrieval-augmented generation, tool calling, agents, or model evaluation techniques.
Healthcare, pharmacy, or other regulated-industry experience, including exposure to PHI or FHIR, HL7, NCPDP, or X12.
Forward-deployed, customer-facing, field, or solutions engineering experience.
Startup, founding engineer, or zero-to-one product development experience.
Depth in cloud architecture, DevSecOps, APIs, distributed systems, or data engineering.
Experience creating evaluation datasets, quality metrics, monitoring, and feedback loops for AI-enabled systems.
Experience mentoring senior engineers or providing technical leadership within a product or delivery pod.
Experience evaluating build-versus-buy options using quality, risk, operating cost, and business outcomes.

Responsibilities

Work directly with McKesson teams to observe workflows, identify edge cases, and understand areas of material cost, risk, and opportunity.
Translate ambiguous business problems into clear technical plans and test high-risk assumptions through focused prototypes or minimum viable solutions.
Design, build, integrate, test, deploy, and operate full-stack applications, APIs, services, data pipelines, and enterprise system integrations.
Apply AI capabilities where they improve outcomes, including large language models, retrieval-augmented generation, tool calling, AI agents, and evaluation frameworks.
Build evaluation datasets from representative historical work, measure solution quality, and identify failure modes before production deployment.
Incorporate security, privacy, compliance, monitoring, observability, and incident response into solutions from the beginning.
Own technical direction and make architecture decisions within enterprise guardrails; use AI coding tools in daily engineering work.
Mentor engineers, contribute reusable patterns and tools, and transfer proven solutions with documentation and operational readiness for long-term support.
Evaluate whether to use existing products, compose available capabilities, or build new solutions based on quality, risk, cost, and measurable outcomes.