Senior Principal, Design Engineering

Celestica International LP•San Jose, CA

2d•$210,868 - $329,857

About The Position

We are seeking a high-impact, visionary Senior Principal AI Engineer to architect, design, and oversee the next generation of our enterprise hardware intelligence platform. In this role, you will be the change agent across our global R&D organization, bridging the gap between advanced Generative AI and complex hardware engineering. Reporting into the Office of the VP of Engineering, you will spearhead the technical development of our "Celestica-Tuned Enterprise LLM," transforming massive, highly complex repositories of global engineering data into active, predictive, and autonomous design intelligence. This is a high impact, high visibility technology role. You will serve as the lead and technical authority responsible for building the domain-specific data pipelines and architecting the cutting-edge Topological and Spatial Retrieval-Augmented Generation (RAG) frameworks. Your mission is to develop a secure, multi-agent AI system and build a digital twin of the hardware design process, enabling real-time simulation and predictive analysis of board performance. This system enables engineering teams to interact natively with design tools and legacy knowledge. Working closely with hardware Subject Matter Experts (SMEs), you will translate schematics, layouts, board logs, and multi-tool diagrams into an intelligent platform that our designers use daily to eliminate board spins, automate compliance, and drastically accelerate time-to-market.

Requirements

10+ years of rigorous software engineering experience, including a minimum of 5 years of dedicated, hands-on production coding in Large Language Model (LLM) application development, Natural Language Processing (NLP), or advanced Machine Learning architectures.
Proven track record of architecting and deploying AI-driven tools specifically tailored for the Electronic Design Automation (EDA) and hardware engineering lifecycle. You must understand how to ingest and manipulate complex hardware data (e.g., schematics, PCB layouts, netlists, and BOMs).
Expert-level coding proficiency in Python and deep learning frameworks (PyTorch/TensorFlow).
Proven ability to design, build, and scale high-performance vector databases (e.g., ChromaDB, Milvus, Pinecone).
Mastery of advanced LLM orchestration and agentic frameworks (e.g., LangChain, LlamaIndex).
Demonstrated experience building complex, multi-database Retrieval-Augmented Generation (RAG) pipelines and autonomous multi-agent systems.

Nice To Haves

Deep experience in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs).
Strong familiarity with the latest LLMs and autonomous AI agents (e.g., Glean, Gemini Enterprise, Vertex AI, and Anthropic Claude).
Comprehensive understanding of the end-to-end hardware engineering process for hyperscalers, including schematics, Signal Integrity (SI), Power Integrity (PI), PCB layout, thermal, and mechanical design.
Familiarity with industry-leading Electronic Design Automation (EDA) and engineering tools (e.g., Cadence Allegro, Siemens EDA, Cadence Sigrity, Keysight ADS, PTC Creo, and Simcenter Flotherm).

Responsibilities

Set the technical standard for secure LLM applications within Celestica, utilizing advanced proprietary data and secure infrastructure to build a highly reliable, zero-hallucination platform for high-stakes engineering.
Architect and integrate digital twin models to simulate and validate physical hardware performance, enabling virtual prototyping and early design cycle optimization.
Act as the primary hands-on developer to design, build, and deploy a multi-database, topology-aware RAG system capable of accurately vectorizing complex hardware design data.
Create specialized parsers and ingestion pipelines that translate dense binary files, netlists, and graphical schematics into high-dimensional graph-based semantic embeddings.
Leverage advanced orchestration frameworks and the Model Context Protocol (MCP) to develop autonomous AI agents that can manage multi-tool workflows across synthesis, physical placement, and manufacturing sign-off.
Engineer data lakes that seamlessly correlate unstructured text (supply chain metadata, manufacturing datasheets, standard compliance checks) with physical engineering physics (layout geometries, pin connectivity).
Work side-by-side with Principal Hardware, SI/PI, and Thermal Engineers to encode deep domain expertise, design rules, and hardware guardrails directly into the AI system's "Agent Skills."