Forward Deployed Engineer, MT Labs
About The Position
McKesson Technology Labs is seeking a Forward Deployed Engineer who combines software engineering expertise with curiosity about how work happens in real-world environments. The engineer will work with McKesson teams to identify valuable problems and turn ambiguous needs into secure, production-ready solutions. This hands-on senior engineering role involves designing, coding, testing, deploying, and operating full-stack products, potentially including user interfaces, APIs, services, data pipelines, enterprise integrations, and applied AI capabilities. The role also provides technical direction, mentors engineers, and may lead a delivery pod. The engineer helps determine whether to use an existing product, combine available capabilities, or build a new solution based on quality, risk, cost, and measurable outcomes. The role includes regular interaction with supported McKesson teams and operational environments. It requires routine computer use and collaboration through in-person and virtual working sessions. Travel percentage and remote, hybrid, or on-site expectations must be confirmed by the hiring leader before publication.
Requirements
- 10+ 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.
- Demonstrated 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 with McKesson teams to observe workflows, identify edge cases, and understand material cost, risk, and opportunity.
- Translate ambiguous business problems into technical plans and test high-risk assumptions using 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, make architecture decisions within enterprise guardrails, and 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 an existing product, compose available capabilities, or build a new solution based on quality, risk, cost, and measurable outcomes.
- Lead a delivery pod when the work requires it.
Benefits
- Competitive compensation package
- Potential annual bonus or long-term incentive opportunities
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What This Job Offers
Job Type
Full-time
Career Level
Senior
