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The US Oncology Network is seeking a Lead Forward Deployed Engineer (FDE) to partner directly with business and operational teams to turn complex, high-value problems into validated AI-enabled solutions. This is a hands-on, business-embedded engineering role for someone who can work comfortably in ambiguity, connect business needs with technology, and rapidly move ideas from discovery to working prototypes.
The FDE leads the front end of the AI delivery lifecycle. You will understand workflows, frame problems, assess technical options, build and test prototypes, and validate value with users. Once a concept is validated, you will define a clear production path and work with AI Application Engineering, Data Engineering, MLOps, architecture, security, compliance, and operational teams to transition the solution for industrialization and scale.
This role requires strong software engineering judgment, broad technical fluency, and exceptional stakeholder engagement. The FDE role provides technical coherence from problem through prototype, preserves the intended business outcome, and ensures downstream teams have the context needed to deliver a scalable, governed solution.
- Embed with practice and business teams to understand workflows, user needs, operational constraints, pain points, and desired outcomes.
- Lead discovery workshops, workflow assessments, and problem-framing sessions with executives, physicians, clinical teams, revenue cycle leaders, operational teams, and subject matter experts.
- Translate ambiguous business needs into clear problem statements, testable hypotheses, technical options, prototype scopes, and measurable success criteria.
- Advise stakeholders where AI, automation, data, or workflow technology is appropriate and clearly communicate capabilities, limitations, risks, and tradeoffs.
- Design pragmatic solution approaches using approved enterprise technologies, architecture patterns, and available data assets.
- Build working prototypes, proofs of concept, and thin-slice solutions that test feasibility and enable meaningful feedback from representative users.
- sound software engineering practices so prototype code, interfaces, and design decisions can be understood, reused, or extended by delivery teams.
- Use AI-enabled patterns such as retrieval-augmented generation, copilots, agents, semantic search, decision support, and workflow automation when appropriate to the problem.
- Demonstrate prototypes, gather user and technical feedback, iterate rapidly, and recommend whether to stop, refine, or advance the work.
- Identify application, data, integration, security, architecture, operational, and governance dependencies early in the engagement.
- Collaborate with AI Application Engineers, Data Engineers, MLOps Engineers, architects, product leaders, security, compliance, and business SMEs to shape an executable delivery path.
- Define solution requirements, dependencies, and acceptance criteria for production delivery.
- Assess production readiness and document risks, decisions, and required controls.
- Lead solution handoff to engineering teams and support successful implementation.
- Drive reuse by capturing patterns, assets, and lessons learned across engagements.
- Contribute to FDE playbooks, prototype standards, reusable asset libraries, and delivery practices that improve speed and consistency.
- Mentor engineers in business discovery, problem framing, rapid prototyping, and technical stakeholder communication.
- Degree or equivalent experience and typically 8+ years of relevant experience in software engineering, solution engineering, solution architecture, technical consulting, enterprise application delivery, or digital transformation.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
- 8+ years of experience building, integrating, troubleshooting, or delivering enterprise software solutions.
- Strong hands-on full-stack engineering and troubleshooting experience across modern web applications, APIs, data, AI services, identity, security, and cloud infrastructure.
- Hands-on experience with React, Node.js, TypeScript, Python, and modern web application architecture.
- Proficiency in API diagnostics and browser-based troubleshooting, including network requests, authentication, application state, errors, and performance issues.
- Hands-on experience with Snowflake and Databricks, including data pipelines, lineage, reconciliation, data quality, and performance tuning.
- Experience using logs, metrics, traces, dashboards, and alerting to perform root-cause analysis and validate end-to-end solution behavior.
- Demonstrated ability to diagnose issues across applications, integration, data, AI, security, and infrastructure layers and determine the appropriate path to resolution.
- Demonstrated experience translating ambiguous business problems into working technical solutions, prototypes, or proofs of concept.
- Experience designing or prototyping AI-enabled solutions such as copilots, agents, retrieval-augmented generation, semantic search, decision support, or workflow automation.
- Experience using AI coding assistants, such as Claude or comparable tools, to accelerate development while reviewing, testing, securing, and refining generated code to meet McKesson engineering and quality standards.
- Sound understanding of software delivery, enterprise integration, security, privacy, Responsible AI, data governance, operational readiness, and supportability.
- Experience working directly with business leaders, subject matter experts, end users, and cross-functional engineering teams.
- Ability to evaluate technical alternatives, explain tradeoffs, and recommend fit-for-purpose approaches without complete requirements.
- Excellent written, verbal, facilitation, and presentation skills, with the ability to communicate complex technical concepts to technical and nontechnical audiences.
- Ability to operate independently across complex or highly visible initiatives while keeping stakeholders aligned on outcomes, scope, risks, and ownership.
- Healthcare industry experience supporting oncology, clinical operations, revenue cycle, patient access, care management, provider operations, or related domains.
- Experience with Azure AI services, Databricks, Python, C#, TypeScript, REST APIs, ServiceNow, Salesforce, Power BI, or comparable enterprise technologies.
- Experience applying retrieval-augmented generation, prompt and response evaluation, agentic workflows, semantic search, or AI guardrails.
- Experience working in regulated environments and applying Responsible AI, cybersecurity, privacy, compliance, and data governance requirements.
- Experience with Agile, product-led, innovation lab, consulting, or pod-based delivery models.
- Ability to learn unfamiliar business domains and technology environments quickly.
Strong customer and business empathy, outcome orientation, engineering judgment, and ability to influence without direct authority.
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Working Conditions
Flex & Connect work model with a requirement to be in the office two (2) days per week.
We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson,
Our Base Pay Range for this position
$122,100 - $162,800
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