Forward Deployed Engineering Manager

Job Title

Forward Deployed Engineering Manager

Location

Remote or Washington, D.C.

Reports to: VP, Strategy & Transformation

About The Company

Our client is a technology organization that delivers digital health and smart spaces solutions for healthcare providers, payers, life sciences organizations, and enterprises. It offers an integrated platform that connects people, data, and workflows to improve care coordination, patient engagement, and operational efficiency. Its portfolio includes virtual care, remote patient monitoring, care management, interoperability, and IoT-enabled workplace safety and facilities management.

Job Summary

The Forward Deployed Engineering Manager is a forward-deployed transformation leader responsible for bringing modern transformation methodologies and AI-enabled workflow platforms to life inside organizations. This role also reflects the expectations of an FDE Manager, capable of full-stack AI implementation, leading discovery and design sessions, and ensuring end-to-end delivery from requirements through post–go-live optimization.

Operating at the intersection of business, technology, and AI, this role leads Global Design sessions to reimagine how workflows operate across complex enterprises, then customizes a standardized AI-enabled workflow library to reflect each organization’s operating model. This includes translating discovery findings into functional and technical requirements, leading implementation execution, and providing ongoing oversight following deployment.

You will work directly with business and technology executives to translate strategy into design, validate impact through rapid configuration, and ensure each deployment achieves measurable outcomes in speed, quality, and cost. Success in this role requires the ability to operate across both functional and technical domains—including healthcare payer workflows, ETL and software engineering fundamentals, and agentic AI workflow design.

Key ResponsibilitiesGlobal Design and Future-State Definition

• Lead Global Design sessions with cross-functional teams to map current workflows, identify inefficiencies, and define future-state architectures.
• Apply structured transformation methodologies to guide objectives and align stakeholders.
• Define how AI agents, data signals, and human expertise interact within redesigned workflows, including decision logic and governance requirements.
• Conduct discovery assessments to elicit business needs, translate them into detailed functional and technical requirements, and ensure alignment between business, IT, and AI Engineering.
  
  

Standardized Workflow Library Customization & Validation

• Tailor the standardized AI-enabled workflow library to each environment, configuring orchestration logic, integration points, and performance metrics.
• Validate end-to-end process design through scenario testing and outcome modeling before production deployment.
• Document configuration, impact metrics, and key learnings for iterative improvement.
• Apply engineering fundamentals—including ETL pipeline understanding, data flow dependencies, and system integration patterns—to ensure successful workflow implementation.
• Contribute to full-stack AI implementation by translating requirements into agentic workflows, signals, and orchestration logic.
  
  

AI-Enabled Deployment and Value Realization

• Support deployment of AI-enabled workflows, coordinating across business, IT, and AI Engineering to ensure seamless implementation.
• Optimize orchestration logic, triggers, and decision pathways to ensure agents perform reliably and deliver measurable results.
• Monitor early adoption and performance to validate impact and ensure defined outcomes (throughput, accuracy, experience) are achieved.
• Capture insights from field execution to refine transformation design patterns and inform future platform enhancements.
• Provide post-go-live guidance and oversight to ensure operational stability, continuous improvement, and sustained value realization.
  
  

Requirements

• 7–10 years of experience in enterprise transformation, consulting, or AI-enabled operations.
• Strong understanding of business process re-engineering, workflow orchestration, and data integration.
• Familiarity with LLM-based systems, orchestration frameworks, and automation platforms.
• Proficiency in Python, APIs, and cloud architecture (Azure, AWS, or GCP) nice to have.
• Demonstrated ability to lead workshops and translate strategic objectives into actionable technical design.
• Excellent communication and executive presence, credible with both business and technology leaders.
• Healthcare experience preferred; intellectual versatility essential.
• Healthcare payer domain knowledge strongly preferred, with the ability to support UM/CM or adjacent operational workflows.
• Technical grounding in ETL, software engineering concepts, and end-to-end system integration.
• Experience delivering full-stack AI solutions—from requirements definition through deployment and optimization.