Principal Engineer, Solutions Architect Lead – Industrial & Embedded IoT, Edge AI On‑Prem Appliance

Own solution blueprints: Design, develop, document, and maintain reference designs and delivery playbooks that accelerate repeatable GenAI/Hybrid‑AI deployments on AI on-prem Appliance across priority verticals (e.g., industrial, retail, smart spaces, healthcare, public sector). Vertical customization: Partner with Product Management, Account Managers, Regional teams, Engineering and field teams to adapt blueprints to customer‑specific requirements/KPIs , data, infrastructure, privacy/security, and compliance constraints; drive solution acceptance and production hand‑off. Be hands‑on: Proficient in SW skills; Design and develop reference designs, Build POCs and pilot systems; instrument, profile, and optimize models and pipelines end‑to‑end (latency, throughput, accuracy, cost, power/thermals, footprint). Multi‑model AI expertise: Apply and integrate LLMs, VLMs, VLAs, CV/CNN, and NLP stacks; select/quantize/prune/distill models; tune adapters; and integrate guardrails, retrieval, and evaluation frameworks. Solutions focus: Deliver production‑grade patterns for video analytics, enterprise GenAI (RAG, document AI, code & task copilots), and multimodal agents that interact with enterprise systems and tools. Hybrid AI architecture: Partition workloads across on‑device AI for a variety of edge devices, , on‑prem/edge boxes, and cloud AI; orchestrate data, models, and agents; design for offline/online modes, observability, and device management. Customer focus: Engage with customers from discovery to scale‑out; translate business objectives into measurable technical requirements; lead design reviews, roadmap alignment, and executive readouts. Cross‑functional leadership: Work with CE, Product Management, Solutions, Platform SW, Performance, Security, and Research to leverage existing knowledge and infrastructure, land features in reference design releases, software roadmaps and deliver outcomes. Innovation and Thought leadership: Create best‑practice guides, participate at leading industry events and workshops; stay abreast on the latest tech developments in the field, be aware of the competitive landscape, mentor engineers Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 8+ years of Systems Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 7+ years of Systems Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field and 6+ years of Systems Engineering or related work experience. Proven track record (Principal level) architecting and shipping systems‑level AI solutions that combine application, runtime, and platform considerations (performance, power, memory, cost, security). Deep hands‑on proficiency with LLM/VLM/CV/NLP model lifecycles: selection, fine‑tuning/LoRA, compression/quantization, runtime integration, and hardware‑aware optimization and deployment Expertise designing hybrid AI: placing workloads across device/edge/cloud; knowledge of data pipelines, streaming/vision services, vector/RAG stores, feature stores, and observability. Strong software engineering foundations (Python/C++), containerization, AI accelerators, and profiling tools; fluency with modern inference/runtime stacks. Customer‑facing experience on launching new AI-enabled products working with engineering and product management teams to drive POCs to production with clear KPIs Excellent communication and leadership skills to influence across teams and with customers/peers and executives. Experience building video analytics pipelines (tracking, action/event detection, privacy filters), and integrating them with enterprise VMS/IoT backbones and AI based vision solutions Designing and deploying agentic AI systems (multi‑agent planners, tool ecosystems, policy/guardrails, eval harnesses); familiarity with orchestration frameworks and agent runtimes. Model/system benchmarking and E2E evaluation (latency/accuracy/cost/power), testing, and operations for AI at the edge. Domain exposure in one or more verticals: industrial automation/IIoT, retail analytics, healthcare operations, smart buildings/cities, logistics, or public sector. Graduate degree in CS/EE/CE or equivalent applied experience. 15+ years of hands-on experience as an individual contributor with 10+ years in the AI/ML fields.