AI Research Engineer

Bachelor’s degree or equivalent experience in computer science, software engineering, or a related field.
Strong Python skills in AI/ML development (eg. Pytorch, tensorflow, NLP, scikit-learn)
Deep experience using generative AI models (Llama, Claude, ChatGPT, Gemini, Deepseek, Mistral), tools (langchain, LlamaIndex, Haystack, Auto-GPT) and supporting infrastructure (Azure, AWS, Hugging Face, or local hosting on GPUs (NVIDIA)).
Experience with LLM model selection, fine-tuning, RAG, tool-use, MCP servers, AI workflows, AI agents, and prompt optimization.
Proficiency with vLLM, TensorRT-LLM, OpenLLM, Llama.cpp or related optimizations for accelerating LLM inference.
Strong ability to collaborate in agile development environments with front-end engineers, full-stack engineers, IT-support, and domain authorities in ESD across a variety of platforms (IoT, microcontrollers, FPGA, GPU).
Strong understanding of RESTful APIs, GraphQL, WebSockets, and databases (SQL/Postgres)
Experience with version control (Git), CI/CD pipelines, and containerized environments (Docker, Kubernetes).
Entrepreneurial approach, with a passion for innovation, customer success, and driving growth in a rapidly evolving market.

Create new AI workflows, agents, frameworks, memory systems, and tool integrations to assist engineers with embedded systems design tasks.
Define and configure LLM model infrastructure; select, curate, and update models to maintain innovative performance in secure environment where protection of IP and customer data is paramount.
Create software for AI backend tools that interface to customer application infrastructure and data systems; develop APIs to AI models and data sources.
Work with internal collaborators and ESD domain authorities to understand system requirements and automate targeted design processes.
See opportunities for creating custom fine-tuned LLM models for specific domain knowledge in embedded systems; work with collaborators to collect, curate, and generate training data, including generating synthetic data; train and deploy custom models for ESD tasks.
Specify and lead high performance computing resources including on-site GPU servers for hosting LLMs, agents, and customer data.
Ensure timely and successful delivery of high-quality, scalable code solutions.
Ensure maintainability and reliability of code.

Understanding of CAD/CAE tools used in fields such as electrical engineering design and ESD.
Prior experience in a manufacturing, design automation, or industrial software environment.