• Design and implement end-to-end data pipeline architecture spanning edge devices, ingestion, processing, storage, and delivery into analytics/AI workloads • Build scalable ETL and data processing frameworks with orchestration, schema management, versioning, and automated data quality controls • Develop real-time and streaming infrastructure supporting event-driven systems, edge-to-cloud synchronization, buffering strategies, and strict latency requirements • Own DevOps and infrastructure engineering, including CI/CD pipelines, infrastructure-as-code, container orchestration, and production deployment workflows • Implement and maintain security architecture across the stack, including access controls, secrets management, network segmentation, vulnerability scanning, and compliance practices • Establish strong observability, monitoring, and operational tooling for distributed systems running across cloud, edge, and enterprise integrations • Support onboarding of complex multimodal data sources including telemetry, time-series, video, audio, LiDAR, and geospatial datasets

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description The Decision Systems Architect plays a critical role in advancing Sena’s decision capabilities by formalizing judgment, reasoning frameworks, and evaluation logic into system-native structures. This role focuses on extending the breadth, depth, and consistency of Sena’s decision logic as new customer use cases, signal types, and business questions emerge. This is a product-embedded role at the intersection of systems thinking, applied reasoning, and AI enablement. - Designing and refining decision frameworks that guide how Sena interprets customer intent - Defining signal mapping logic that structures how business questions are decomposed into actionable signals - Establishing configuration schemas that specify how data must be captured to support decision-grade outputs - Creating evaluation criteria and quality standards used to assess system outputs - Identifying recurring patterns, edge cases, and failure modes, and formalizing them into reusable system logic - Producing structured artifacts that directly inform Product and AI development workflows - Collaborating closely with Product, AI, and Engineering teams to ensure decision logic is encoded effectively into Sena All work in this role is expected to be system-oriented, reusable, and extensible. Qualifications - Strong systems thinking and the ability to formalize complex reasoning - Experience designing frameworks, methodologies, or structured decision logic - The ability to translate ambiguous problems into clear, explicit structures - Comfort working in highly autonomous, fast-moving environments - Intellectual curiosity and a willingness to challenge established methods Requirements - Candidates may come from applied research or technically rigorous domains where deep reasoning, formal abstraction, and the hands-on design of complex decision systems were core to the work. Culture Fit - Take initiative and operate with high ownership - Produce high output with limited resources - Prefer autonomy and accountability - Are inventive and comfortable rejecting outdated approaches - Are deeply focused and driven by hard problems How Candidates Are Evaluated - Their ability to structure and reason through ambiguous problems - The clarity, generalizability, and technical rigor of the frameworks they design - How effectively their thinking can be translated into system-level logic - The quality and rigor of their judgment under real-world constraints - Their ability to independently design, execute, and iterate on decision frameworks Summary The Decision Systems Architect helps define how Sena thinks as it continues to expand across new domains and use cases. This role is central to shaping the system’s long-term decision intelligence and is designed for individuals who want to work at the intersection of reasoning, systems, and product design.

• Architect and implement AI solutions leveraging Microsoft Copilot, AI Agents, and Azure OpenAI. • Collaborate with business stakeholders to identify AI opportunities and define solution roadmaps. • Design AI frameworks for automation, productivity, and decision intelligence across enterprise systems. • Provide technical leadership to development teams on AI adoption, best practices, and integration. • Evaluate emerging technologies and recommend AI-driven strategies for digital transformation. • Ensure compliance with security, governance, and responsible AI practices.

Our Mission: Cloud-only AI is hitting real limits—cost, latency, privacy, and scale. We’re building the next generation of Hybrid Collaboration AI systems, where local AI and cloud AI collaborate as one. This role is about defining how AI workloads are intelligently planned, routed, and executed across environments. If you want to shape the architecture that moves AI beyond cloud-only constraints, this role sits right at the center. Build the hybrid AI collaboration layer connecting local AI and cloud AI. Design dynamic orchestration for planning, routing, and execution. Optimize AI workflows for latency, cost, privacy, and reliability. Enable agent-based systems with local planning and hybrid execution. Partner with product, systems, and runtime teams to ship production-ready platforms. Engage with industry-leading AI providers for co-engineering. Work with internal engineering teams and external partners, customers, and ISVs to deliver optimized AI solutions using Intel products. Research technical trends and utilize deep domain and market segment expertise to prototype solutions and develop AI software to enable hybrid collaboration AI. Optimize performance of AI models through deep knowledge and expertise of AI frameworks, algorithms, models, and related hardware. Research, develop, and adjust new or existing AI models, code, parameters, and/or quantization to address issues and modify operations to enhance hybrid performance. Partner with AI algorithm and framework engineers as needed to optimize end-to-end AI models to Intel hardware features. Serve as a trusted technical advisor and provide technical enabling. Partner with Intel software and hardware product development teams to accelerate and optimize future products in AI and HPC domains. Deliver competitive and differentiated benchmark collateral and identify and drive key workloads into product requirements and architect innovative projection methodologies and tools.