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

Qualcomm•San Diego, CA

2d•Onsite

About The Position

Qualcomm is a global leader in connected intelligent edge, focusing on AI, edge computing and connectivity. Our fast‑growing Industrial and Embedded IoT (IE‑IoT) BU leads the transformation of industries through intelligent edge solutions that combine connectivity, compute, and AI. We expand our global talent in applied edge AI to support our customers’ digital transformation across many verticals and industries. AI on‑prem Appliance is a new product line under IE‑IoT BU. This advanced AI solution is designed for computer vision, generative AI inference and Agentic AI workloads on dedicated on‑premises hardware—allowing sensitive customer data, fine‑tuned models, and inference loads to remain on premises. It combines the accessibility and performance of a datacenter inference server with power efficiency, form factor, privacy, personalization, and control of an on‑premises AI solution. Qualcomm AI Inference Suite provides ready‑to‑use AI applications and AI agents, tools, and libraries for operationalizing AI. Qualcomm is building end‑to‑end Edge AI solutions for Generative AI (LLM, VLM, VLA), Agentic AI, Voice AI, workloads that are commercially deployed on AI on-prem Appliance. Applications and use cases span various connected devices in on‑device, on‑prem, and hybrid cloud scenarios. As a Principal Agentic AI Solutions Architect, you will define, develop, document and own scalable and customizable blueprints for different vertical solutions, lead hands‑on prototyping, and collaborate closely with customers, partners, product management, customer engineering and other internal teams to tailor deployments for enterprise customers across industries. We are seeking an experienced, hands-on expert with strong SW skills, solid knowledge of AI hardware, passionate about developing innovative genAI and hybridAI solutions with transformational impact to the industries.

Requirements

Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 8+ years of Systems Engineering or related work experience.
Master's degree in Engineering, Information Systems, Computer Science, or related field and 7+ years of Systems Engineering or related work experience.
PhD in Engineering, Information Systems, Computer Science, or related field and 6+ years of Systems Engineering or related work experience.

Nice To Haves

10+ years of experience in AI/ML (focused on NLP or GenAI), with deep fluency in LLM frameworks, RAG stacks, agentic toolchains, and modern AI development practices.
Proven track record (Principal level) architecting and shipping systems‑level AI solutions that combine application, runtime, and platform considerations (performance, power, memory, cost, security).
Solid foundation and 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
Proven experience designing agent-based systems (single-agent and multi-agent); Familiarity with planning + execution loops, tool use, memory, and self-reflection.
Experience with agent frameworks (e.g., LangGraph-style DAGs, MCP-like protocols, custom orchestrators).
Understanding of emergent behavior, coordination, and agent safety bounds
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.
Real world deployment experience deploying AI systems on edge, embedded, or on prem platforms.
Familiarity with model optimization (quantization, sparsity, scheduling, runtime selection)
Designing agents that interact with tools, APIs, browsers, simulators, or environments.
Experience with closed loop systems (observe → decide → act → learn).
Knowledge of state management, episodic memory, and long horizon tasks.
Understanding of latency, memory, power, and cost constraints
Awareness of AI safety, alignment, and controllability in autonomous systems.
Experience with guardrails, policy enforcement, and human in the loop designs
Research Mindset with Product Focus. Ability to translate research ideas into deployable systems.
Comfortable reading and implementing from academic papers.
Experience balancing innovation vs. production constraints
Excellent communication and leadership skills to influence across teams and with customers/peers and executives.
Model/system benchmarking and E2E evaluation (latency/accuracy/cost/power), testing, and operations for AI at the edge.
Background with Qualcomm AI platforms and heterogenous acceleration; familiarity with on‑device inference and memory/power budgeting.
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.

Responsibilities

Own AI solution blueprints: Design, develop, document, and maintain reference designs and delivery playbooks that accelerate repeatable Agentic AI/GenAI/Hybrid‑AI deployments on AI on-prem Appliance across priority verticals
Lead the design of GenAI blueprints using LLMs, RAG, and agentic workflows to solve enterprise challenges across domains like knowledge management, customer support, and analytics.
Agentic AI: Architect and implement agentic workflows (multi‑agent planning, tool use/function calling, memory, safety) using Qualcomm platforms and industry frameworks; define KPIs and evaluation loops.
Architect intelligent multi-agent systems with long-horizon reasoning, tool use, and orchestration frameworks (e.g. LangChain, LlamaIndex) to automate complex workflows.
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).
Deliver hands-on prototypes, demos, and reference architectures (e.g. chatbots, summarizers, multimodal assistants) that scale to production and showcase Qualcomm’s GenAI stack.
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.
Build and optimize RAG pipelines with vector databases and semantic search to enable fast, context-rich LLM responses; fine-tune and deploy models on Cloud AI 100 Ultra and other accelerators.
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