Trissential

• Design and build scalable backend services supporting analytics and clinical applications • Own and optimize the semantic layer across SAS, MCC, and Power BI environments • Develop high-performance Power BI datasets, reports, and dashboards (advanced DAX required) • Maintain and modernize Tableau dashboards, transitioning them into Power BI where appropriate • Query and model large datasets using GCP BigQuery for analytics consumption • Contribute to microservices architecture ensuring reliability, scalability, and performance • Collaborate with product owners, architects, and engineers to translate business requirements into solutions • Maintain and interpret legacy SAS environments, supporting transition efforts • Perform deep-dive troubleshooting and root cause analysis for data and system issues • Document datasets, reporting logic, and data lineage to enable self-service analytics • Mentor junior engineers and contribute to cross-functional improvement initiatives

• Serve as the system owner and enterprise steward for core business platforms including Aha!, ADO, and Miro • Define, implement, and evolve governance frameworks, configurations, and ways of working aligned to enterprise strategy • Own platform operating models, including access controls, licensing, security, and audit readiness • Administer and enhance system configurations, workflows, and integrations • Drive automation to reduce manual effort and standardize scalable delivery practices • Partner with cross-functional teams to translate business and product needs into optimized system designs • Establish guardrails, templates, and standards to ensure consistent and efficient tool usage • Ensure data integrity and reporting accuracy across time tracking, cost, utilization, and portfolio insights • Serve as the escalation point for complex system, workflow, and integration issues • Lead stakeholder enablement through training, onboarding, and change management initiatives • Evaluate enhancements, prioritize backlogs, and continuously improve platform performance and adoption

• Design and build scalable, resilient back-end services supporting data-intensive clinical and analytics applications • Develop and maintain data pipelines for ingestion, validation, transformation, and replication across systems • Build cloud-native solutions using Google Cloud Platform services such as BigQuery, Dataflow, and Cloud Storage • Collaborate with Product Owners, Architects, and Engineers to translate requirements into robust technical solutions • Package, deploy, and manage applications using Docker containers in a cloud environment • Implement CI/CD pipelines using Azure DevOps and manage code through Git repositories • Create and maintain data catalogs, dashboards, and documentation for datasets and pipelines • Troubleshoot complex system issues using root cause analysis and data investigation techniques • Participate in Agile ceremonies and contribute to DevOps best practices • Mentor junior engineers and contribute to architecture and design discussions

• Lead and facilitate discovery interviews and workshops to deeply understand client processes and operations • Build clear, detailed process models such as swimlanes, value streams, and user journeys • Drive Kaizen, A3, and design-thinking sessions to optimize workflows before implementing AI solutions • Identify and evaluate opportunities for AI, automation, RPA, and low-code/no-code tools • Develop concise and compelling business cases with clear value propositions and measurable outcomes • Recommend prioritization of AI use cases and guide them into structured delivery pipelines • Partner with technical teams to translate business needs into actionable backlogs, prototypes, and pilot programs • Define, track, and analyze pilot metrics to determine scalability and ROI • Maintain well-structured documentation to accelerate delivery from discovery to execution • Coach stakeholders on identifying and prioritizing future AI and automation opportunities

• Partner with business and technical stakeholders to understand workflows, challenges, and success metrics • Translate ambiguous problems into clear technical solutions, designs, and delivery plans • Design and build AI-powered applications using LLMs, APIs, and enterprise data systems • Develop production-grade backend services using Python and frameworks like FastAPI or Flask • Build and maintain RAG systems, including document ingestion and normalization, chunking, metadata, and embedding strategies, vector, keyword, and hybrid search, reranking and relevance tuning, source attribution and grounding • Integrate AI solutions with SharePoint, Microsoft Graph, SQL databases, internal APIs, and business applications • Design secure systems that respect access control, governance, and enterprise compliance requirements • Build observable, reliable, and maintainable AI workflows in production environments • Establish evaluation frameworks for LLM systems (accuracy, groundedness, latency, completeness, and failure modes) • Iterate rapidly through prototypes, pilots, and production releases based on user feedback • Collaborate with engineering, data, security, and product teams to ensure adoption and long-term sustainability • Make pragmatic technology choices across Azure OpenAI, Azure AI Search, and open-source tools • Document architecture, decisions, trade-offs, and operational requirements.

• Design, develop, and deploy production-grade AI/ML and Generative AI solutions • Build and optimize RAG pipelines, embeddings, semantic search, and document intelligence workflows • Develop scalable APIs and full-stack applications supporting AI use cases • Collaborate closely with system architects across the full software lifecycle • Implement LLM evaluation frameworks, monitoring, and performance metrics • Apply AI safety guardrails including content moderation and hallucination reduction • Lead and mentor engineers through design, development, testing, and release cycles • Communicate technical tradeoffs and progress clearly to stakeholders

• Develop, enhance, and maintain GIS application components using C# .NET and ESRI v10.1 toolsets • Support system integrations related to Schneider Electric’s ADMS, including CIM import files • Collaborate closely with the GIS Applications Team Leader and broader GIS team on ongoing initiatives • Participate actively in daily standups, sprint planning sessions, and code reviews • Troubleshoot, debug, and resolve application issues in complex GIS environments • Provide weekly status reports outlining progress, risks, and dependencies • Contribute to documentation and promote best practices in GIS application development

• Define and maintain target-state architecture for inpatient video visit and virtual care platforms. • Develop multi‑year architecture roadmaps aligned with enterprise digital health strategy. • Design end‑to‑end integrations across EHRs, IAM, scheduling, bed management, patient portals, and clinical communication tools. • Establish interoperability patterns using HL7, FHIR APIs, and event‑driven architectures. • Evaluate and govern vendor-supported telehealth platforms for scalability, security, and architectural fit. • Architect secure data flows that meet HIPAA, HITECH, and NIST cybersecurity standards. • Define standards for bedside devices, mobile carts, tablets, and audiovisual peripherals. • Guide hybrid cloud and on‑prem deployment models, ensuring high availability and disaster recovery.

• Define and execute the product strategy and roadmap for a conversational AI platform supporting patient‑facing use cases • Lead delivery of an MVP launch and manage iterative quarterly enhancements • Own the full product lifecycle including strategy, planning, execution, monitoring, and optimization • Translate business and patient needs into clear product requirements, user stories, and acceptance criteria • Drive Agile product development processes including backlog management, sprint planning, and prioritization • Partner cross‑functionally with engineering, design, clinical, and business stakeholders • Collaborate with engineering teams leveraging Gemini (via Vertex AI) and retrieval‑augmented generation (RAG) pipelines • Define and track KPIs, success metrics, and product performance indicators • Proactively identify risks, plan contingencies, and resolve complex product and stakeholder challenges • Act as the primary liaison between technical teams and non‑technical stakeholders

• Partner with business and technical stakeholders to define, document, and deliver high‑quality digital products • Analyze business needs and translate them into clear business requirements, technical specifications, acceptance criteria, and user experience requirements • Perform cost, benefit, and risk analysis across the product lifecycle and recommend solution alternatives • Collaborate closely with Engineering and IT teams to support solution design, development, integration, and validation • Utilize systems engineering and Agile methodologies consistently throughout the product and project lifecycle • Establish clear product scope boundaries and manage scope, timelines, and delivery expectations • Conduct data analysis and root cause analysis to proactively identify issues and propose solutions • Interpret vendor‑provided solution architectures and translate them into integration requirements and technical scope • Support deployment coordination and change management activities for assigned products • Serve in a Product Owner capacity, prioritizing backlogs, mitigating roadblocks, and driving sprint and release goals