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Owns client implementations from post-sales handoff through go-live, serving as the primary client contact and coordinating internal and external teams. The role manages product configuration, timelines, priorities, risks, stakeholder alignment, and client communication for complex healthcare implementations involving large data sets. Responsibilities include facilitating delivery, escalating challenges, connecting clients with training, and ensuring a successful handoff to post-implementation teams.

Manages standard client onboarding and implementation projects for mid-market healthcare clients. Serves as the primary client contact, coordinates configurations, data integrations, deployments, training, and internal delivery teams. Tracks timelines, deliverables, risks, status reporting, and documentation while ensuring projects remain aligned with client goals. The role also supports process improvements and develops expertise in healthcare environments, Qualtrics products, and implementation methodologies.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

Designs, deploys, and scales production AI agents and agentic workflows within enterprise Java and Spring applications. Establishes evaluation, monitoring, guardrail, safety, testing, observability, deployment, and operational standards for LLM systems. Leads architectural decisions across AI services and microservices, productionizes models, evaluates emerging technologies, mentors engineers, and influences technical strategy across teams.

Build and deploy production AI agents, agentic workflows, and ML-enabled enterprise features within Java/Spring microservices. Implement evaluation, monitoring, guardrails, safety, testing, and observability for LLM applications. Collaborate with AI scientists, engineers, product managers, and technical specialists to productionize models, integrate RAG and tool calling, prototype solutions, and improve AI capabilities. The role also involves design reviews, agile planning, software quality ownership, and staying current with emerging AI technologies.

Designs, develops, tests, documents, and deploys software enhancements for Clinical Solutions products. The role includes front-end development, API and database work, troubleshooting, release preparation, peer reviews, and application support. It mentors developers, promotes engineering standards, coordinates issue resolution with business and IT teams, and contributes to agile planning. The engineer is also expected to apply AI-first development practices and communicate best-practice technical solutions.

Own the post-sales client onboarding and implementation process, acting as primary client contact. Coordinate cross-functional teams, manage timelines, configure product solutions, identify and escalate risks, and ensure successful go-live and handoff to delivery while driving high client satisfaction and training connections.

Design, develop, and evaluate conversational AI agents and generative AI products. Collaborate with AI scientists, ML engineers, and Responsible AI/Model Validation teams to ensure state-of-the-art, ethical solutions and support deployment and integration of conversational systems.

Develop, test, and maintain software features for the Hyperlift Stars Platform using AI tools, collaborating with teams on implementation and support while ensuring quality through testing and documentation.

Lead the design, development, and delivery of complex features for the Hyperlift Stars Platform. Responsibilities include setting technical direction, architectural guidance, and improving the efficiency and reliability of data-intensive software systems.