AI/ML Engineer – LLM & AI Harness Engineering - 100% Remote US

Wilson•Middletown, PA

20h•Remote

About The Position

A leading global technology and engineering company is seeking an experienced AI/ML Engineer to join its Digital Data Networks organization and help build practical AI solutions that accelerate engineering productivity, technical data analysis, and decision-making. This is a highly hands-on role focused on AI/LLM harness engineering. You will build Python-based solutions around existing AI models, incorporating LLMs, Retrieval-Augmented Generation (RAG), AI agents, tool calling, structured workflows, evaluation, and guardrails. The ideal candidate combines strong AI/ML engineering skills with the ability to understand and work with complex technical and engineering data.

Requirements

Bachelor's degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related technical discipline.
Strong hands-on experience with Python for AI/ML development, data processing, model training, validation, and automation.
Solid understanding of machine learning and deep learning, including neural networks, CNNs, and LSTM/recurrent architectures.
Experience with AI/ML frameworks such as PyTorch, TensorFlow, or equivalent.
Understanding of GPU-enabled AI/ML development and CUDA, particularly in NVIDIA environments.
Practical knowledge of Large Language Models (LLMs) and experience working with open-source and/or commercial AI models.
Experience with local LLM environments or model-serving tools such as Ollama, LM Studio, llama.cpp, or equivalent.
Experience with Hugging Face, LangChain, or similar AI/LLM frameworks.
Strong understanding of Retrieval-Augmented Generation (RAG) and experience implementing RAG-based workflows.
Ability to design AI-agent/harness architectures incorporating RAG, tool calling, workflow orchestration, evaluation, guardrails, and external data sources.
Strong analytical and problem-solving abilities with a focus on validating AI outputs and understanding model limitations.
Excellent communication skills and the ability to explain AI concepts and technical tradeoffs to engineering stakeholders.
Ability to work independently, learn quickly, and collaborate effectively within a global technical organization.

Nice To Haves

Experience applying AI/ML or LLMs to engineering, signal-integrity, measurement, simulation, test, or product-development datasets.
Experience developing custom AI tools for engineering measurement, simulation, test, or product-development workflows.
Experience using Generative AI to support product design, engineering parameter optimization, or design iteration.
Experience using AI to identify product defects, performance issues, root causes, and corrective actions.
Experience with AWS-based AI/data environments, including databases, queues, notebooks, or related infrastructure.
Strong experience with Python/Jupyter notebooks for rapid prototyping and technical demonstrations.
Experience evaluating user or engineering performance with and without AI assistance.
Understanding of GPU resource planning and compute constraints impacting AI/ML development.
Hands-on experience with LLM fine-tuning, domain-specific model adaptation, training-data development, model serving, or GPU optimization.
Experience working in high-speed interconnect, cable assembly, signal integrity, or related engineering/product-development environments.

Responsibilities

Develop and validate Python-based AI/ML and LLM workflows for engineering analysis, technical data processing, automation, and decision support.
Build model training and validation pipelines using open datasets and adapt approaches for engineering datasets such as s-parameters, VNA, simulation, test, and other measurement data.
Apply machine learning and deep learning techniques, including neural networks, CNNs, and LSTM/recurrent models, to practical engineering challenges.
Develop LLM workflows for data parsing, summarization, extraction, classification, and structured outputs using local or hosted AI models.
Design and implement RAG solutions that ground AI responses in trusted engineering documents, datasets, and approved knowledge sources.
Build AI-agent and LLM harness workflows incorporating task routing, tool calling, workflow orchestration, evaluation, and guardrails.
Develop or integrate custom tools that allow AI workflows to interact with engineering and technical data sources.
Collaborate with signal integrity, product development, testing, manufacturing, and operations teams to identify opportunities for AI automation and decision support.
Translate technical requirements into reliable, reusable AI workflows and prototypes.
Document AI workflows, assumptions, validation approaches, limitations, and recommended next steps.
Evaluate AI-generated results, identify limitations or risks, and make data-driven recommendations for improvement.