• Design detailed, end-to-end technical solutions aligned to project goals, enterprise architecture, and technology standards • Develop solution roadmaps, technical architectures, and design documentation • Perform solution design validation, debugging, and performance analysis • Lead and support proof-of-concept (POC) development to validate proposed solutions • Provide technical leadership to development and engineering teams throughout the project lifecycle • Review and validate solution designs from team members to ensure quality and consistency • Collaborate with development teams to guide solution implementation, integration, and deployment • Document and promote best practices and reusable solution patterns • Support BPA/task order kick-offs, including preparation of agendas and meeting documentation • Ensure deliverables meet contractual, quality assurance, and reporting requirements • Support cloud-based architectures across IaaS, PaaS, and SaaS platforms • Assist in DevOps and CI/CD pipeline implementation and continuous improvement • Ensure solutions meet federal security, privacy, and compliance requirements (including HHS policies and Section 508) • Support system transition, deployment, and operational readiness activities • Assist in operations and maintenance (O&M) planning and sustainment

• Engage with clients to understand their business objectives, challenges, and pain points. • Conduct in-depth discovery sessions to gather requirements and define the scope of AI projects. • Design end-to-end automation solutions that align with clients' specific needs and objectives. • Act as the primary technical advisor to clients, providing insights on AI best practices, potential use cases, and the feasibility of AI projects based on available data and resources. • Develop and deliver compelling presentations and demonstrations of AI solutions to clients. • Collaborate with data scientists and engineers to create AI prototypes and PoCs that showcase the capabilities and potential impact of proposed solutions. • Build and maintain strong relationships with clients, understanding their evolving needs, and identifying opportunities for future AI projects and engagements.

• Work directly with customer stakeholders to understand business and technical objectives • Design architectures across AWS, Azure, and GCP • Lead discovery workshops and architecture assessments • Translate complex technical challenges into practical roadmaps • Serve as a trusted advisor to engineering leaders and executives • Lead cloud cost optimization initiatives for enterprise customers • Identify architectural opportunities to improve efficiency and reduce spend • Design governance frameworks that sustain long-term savings • Help customers establish FinOps best practices • Leverage Helias and other optimization tools to drive measurable outcomes • Help customers adopt AI across engineering and operational workflows • Design solutions that incorporate AI-assisted automation and decision-making • Evaluate AI technologies, platforms, and tradeoffs • Guide customers on practical AI adoption strategies • Guide engineering teams through architecture decisions • Mentor engineers and architects • Ensure solutions are scalable, secure, and operationally sound • Provide technical leadership throughout modernization programs

• Lead cloud cost optimization initiatives across large enterprise environments • Identify waste, inefficiencies, and architectural opportunities for improvement • Design governance frameworks that sustain long-term cloud efficiency • Analyze cloud usage patterns and spending trends • Partner with engineering teams to implement optimization recommendations • Work with cloud cost and usage data at scale • Design and optimize reporting, analytics, and decision-support systems • Build insights that drive cost optimization decisions • Collaborate with customers on cloud financial visibility initiatives • Lead discovery sessions and optimization reviews • Present recommendations to technical and business audiences • Build trusted advisor relationships with customers • Guide engineering and data teams • Establish best practices around cloud efficiency • Mentor team members • Drive architecture decisions that improve both technical and financial outcomes