Role Description As a Solution Architect, you'll act as the emissary of the application development process and help design the overall ecosystem. You'll architect, design, develop, and implement applications and solutions to meet the most critical and complex requirements. As a Solution Architect, you'll provide technical oversight to design, development, and testing activities. It will be up to you to ensure that applications are reliable, scalable, and secure. - Develop and implement technical best practices for data movement, data quality, data cleansing, and other ETL-related activities. - Design and implement solutions requiring integration of multiple platforms, operating systems, and applications across the enterprise. - Ensure efficient design of systems; maintain and improve current portfolio of solutions. - Lead and mentor other architects and analysts in the support of the above responsibilities. - Develop ideas and solutions within different situations. - Recommend changes based on results to increase performance. - Serve as a technical lead in the design and implementation of new enterprise solutions. - Develop and ensure adherence to locally defined standards for all developed components. - Read Data Models to translate them into ETL or ELT loads into Data Warehouses and Data Marts. - Help administer and maintain the ETL environment in a support role with DBAs. - Proficiency in requirements analysis, report building, or other data management related experience. - Advanced knowledge of physical database design and data structures. - Expert knowledge of one or more database management systems. - Knowledge of interface engines and protocols. - Knowledge of commonly used concepts, practices, and procedures as they relate to information systems design, engineering, administration, and management. - Provide thought leadership regarding applications of technology to business needs. - Work directly with customers, both internal and external, to understand their needs and convey those needs to other team members. - Have a strong understanding of many aspects of the business. - Work across teams to build consensus on direction and timing. - Drive all aspects of system design including performance, scalability, security, etc. - Coordinate cross team dependencies. - Work with external vendors to ensure deliverables meet company needs and standards. - Enforce industry and company best practices. - Own the development, maintenance, and enforcement of enterprise standards around development practices, processes, and tools. - Work with Enterprise Architecture to ensure solutions are in alignment with current needs and future objectives. - Build out proofs of concepts in order to “fail fast” on complex projects. - Work with cross-functional resources such as UX Designers, DBAs, Infrastructure, etc. - Serve as a technical subject matter expert. - Develop data quality components and establish and/or conduct unit tests. - Assist in building technical roadmaps. - Develop dimensional data models to support reporting through BI platform. - Communicate with stakeholders across the organization to understand business requirements and ensure data architecture solutions are integrated within existing systems and workflows. - Keep abreast with new data management technologies and industry trends; recommend improvements to existing data architecture solutions to ensure solutions remain effective and efficient. - Develop data quality components and establish and/or conduct validation testing. Remote eligible. The job summary and responsibilities listed above are designed to indicate the general nature of the work performed within this job. They are not designed to contain or be interpreted as a comprehensive inventory of all job responsibilities required of employees assigned to this job. Employees may be required to perform other duties as assigned. Qualifications - Bachelor's degree in Computer Science, Information Technology or related field or 5 years of related job or industry experience in lieu of degree. - Eight to ten (8-10) years experience with ETL automation, data management, warehousing Reporting systems or interface design, development and maintenance. - Five (5) years experience in leading the above process within a team environment with multiple team members. - Experience with data management platforms (e.g. SSIS, SSAS, InfoSphere, Talend, Informatica, SAS). - Experience with HL7, X12, XML, XSLT, and XSD. - Experience with Service Bus Architecture and transaction management systems such as Microsoft BizTalk Server, Microsoft Message Queue, Edifecs or similar.

• Respond to customer product inquiries via telephone or in written, internet-based email • Resolve customer concerns raised during installation, operation, maintenance or product application or compatibility matters • Document customer communication and recurring technical issues to support product quality programs and product development

• Design, build, and deploy intelligent backend services using Python and Azure AI Foundry. • Consume Force Account outputs by API, ensuring the system can accurately display and act on data classification and extraction. • Engineer and optimize Retrieval-Augmented Generation (RAG) pipelines over Standard Operating Procedures (SOPs) to provide contextual, accurate, and actionable insights to users. • Deliver assistive intelligence directly on the case, seamlessly integrating AI outputs into the end-user's workflow. • Design and strictly enforce a redact-before-model discipline, ensuring that all Personally Identifiable Information (PII) is stripped from payloads before data ever reaches a foundational model. • Collaborate with product, design, and engineering teams to shape AI features and translate business goals into technical execution. • Proactively identify AI-driven automation and product improvement opportunities, building prototypes to prove value. • Mentor junior engineers and champion secure, responsible AI coding practices within your team. • Troubleshoot AI-specific technical challenges, including prompt injection risks, model latency, and data leakage. • Guide team members on model selection, evaluation, fine-tuning, and Gen AI usage. • Actively evaluate emerging LLM tooling, agentic frameworks, and open-source developments for applicability to client engagements. • Own the architecture of the team's agentic and LLM systems — agent orchestration, tool integration (e.g., Model Context Protocol), retrieval, and human-in-the-loop design. • Design and implement evaluation frameworks for LLM and agentic systems — covering correctness, boundary, and intent checks — as a standard part of delivery. • Drive adoption of the agentic SDLC across the team: select and roll out AI engineering tools, define usage and governance standards, and measure their impact on delivery velocity and quality. • Establish responsible-AI and evaluation frameworks for agentic systems — guardrails, safety testing, human oversight, and model governance.

• Design, build, and deploy intelligent backend services using Python and Azure AI Foundry. • Consume Force Account outputs by API, ensuring the system can accurately display and act on data classification and extraction. • Engineer and optimize Retrieval-Augmented Generation (RAG) pipelines over Standard Operating Procedures (SOPs) to provide contextual, accurate, and actionable insights to users. • Deliver assistive intelligence directly on the case, seamlessly integrating AI outputs into the end-user's workflow. • Design and strictly enforce a redact-before-model discipline, ensuring that all Personally Identifiable Information (PII) is stripped from payloads before data ever reaches a foundational model. • Collaborate with product, design, and engineering teams to shape AI features and translate business goals into technical execution. • Proactively identify AI-driven automation and product improvement opportunities, building prototypes to prove value. • Mentor junior engineers and champion secure, responsible AI coding practices within your team. • Troubleshoot AI-specific technical challenges, including prompt injection risks, model latency, and data leakage. • Guide team members on model selection, evaluation, fine-tuning, and Gen AI usage. • Actively evaluate emerging LLM tooling, agentic frameworks, and open-source developments for applicability to client engagements. • Own the architecture of the team's agentic and LLM systems — agent orchestration, tool integration (e.g., Model Context Protocol), retrieval, and human-in-the-loop design. • Design and implement evaluation frameworks for LLM and agentic systems — covering correctness, boundary, and intent checks — as a standard part of delivery. • Drive adoption of the agentic SDLC across the team: select and roll out AI engineering tools, define usage and governance standards, and measure their impact on delivery velocity and quality. • Establish responsible-AI and evaluation frameworks for agentic systems — guardrails, safety testing, human oversight, and model governance.