Forward Deployed Engineer - Healthcare Transformation

Forward Deployed Engineer

Wolters Kluwer Health has a strong foundation in trusted clinical decision support. We are extending that foundation into outcome-focused solutions that connect clinical content, data, AI, and workflow to help customers improve clinical, operational, financial, and experience outcomes.

The Forward Deployed Engineer is a product-minded, hands-on engineer who works with strategic customers and WK teams to discover high-value problems, build working solutions, validate impact, and turn repeatable patterns into scalable WK Health capabilities. The role reports into Technology and partners closely with Product. This is not a professional services, implementation delivery, or sales engineering role. It is a product-embedded engineering role that learns from real customer environments, accelerates adoption of WK Health platforms, and shapes reusable product direction across the Health division, including AI-enabled capabilities building on the momentum of our Expert AI propositions.

Job Description:

The FDE owns the path from customer problem to technical proof, measured outcome, and product feedback. They build fast, practical solutions while protecting safety, security, quality, and long-term maintainability. No direct reports are expected initially; the role may lead cross-functional virtual squads or technical work streams.

Essential Duties and Responsibilities:

Discover and Frame Outcomes

• Work with customer executives, clinical leaders, informaticists, pharmacists, IT/data teams, and end users to understand workflows, constraints, decision points, and adoption barriers.
• Translate ambiguous needs into outcome hypotheses, use cases, success metrics, acceptance criteria, prototypes, and implementation options anchored in measurable customer value.

Build, Integrate, and Validate

• Design, code, integrate, test, and deploy prototypes and product extensions using WK Health assets, APIs, data, AI capabilities, and platform services.
• Apply healthcare integration patterns where appropriate, including HL7 FHIR, SMART on FHIR, CDS Hooks, secure APIs, and cloud-native approaches.
• Use modern engineering practices, including Spec Driven Development, automated testing, observability, secure SDLC, and AI-assisted or agentic development with human accountability.

Make Field Learning Reusable

• Design with reuse as the default; convert field-built patterns into APIs, connectors, reference architectures, templates, evaluation harnesses, playbooks, and product backlog input.
• Work with Product and Engineering to determine what should become a platform capability, packaged solution, roadmap priority, or retired one-off pattern.

Navigate Responsibly

• Build AI-enabled workflows grounded in trusted content, clear user intent, human oversight, auditability, and post-deployment monitoring.
• Partner with clinical, editorial, security, privacy, legal, and compliance teams to align solutions with WK responsible AI principles, healthcare safety, and data governance expectations.
• Create alignment across WK and customer teams with clarity, humility, curiosity, and judgment; communicate tradeoffs and push for impact without creating unsupported customization or unmanaged risk.

Experience, qualifications:

• Bachelor's degree in computer science, Software Engineering, Biomedical Informatics, Data Science, Health Informatics, Engineering, or equivalent practical experience. Advanced degree preferred but not required.
• 7+ years in software engineering, customer-facing engineering, solution architecture, technical product management, or related product technology roles.
• Production-quality software experience in enterprise &/or regulated environments.
• Healthcare technology experience across clinical workflows, EHR/payer/pharmacy systems, healthcare data platforms, or digital health. (HL7 FHIR, SMART on FHIR, CDS Hooks, RxNorm, SNOMED CT, LOINC, ICD-10, CPT, NDC, Integration with Epic, Oracle Health/Cerner, MEDITECH, Athenahealth, payer platforms)
• Modern software delivery with AI/GenAI, LLMs, RAG, agentic workflows, AI evaluation, prompt/retrieval testing, or responsible AI governance.
• Ability to turn customer or stakeholder problems into…