AI/ML Engineer – LLM & AI Harness Engineering
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Job Description
AI/ML Engineer building Python-based LLM, RAG, and agent harnesses for engineering data and workflows at a global technology company. Supporting automation, analysis, and product-development decisions.
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 engineering datasets such as s-parameters, VNA, simulation, test, and measurement data.
- Apply machine learning and deep learning techniques, including neural networks, CNNs, and LSTM/recurrent models, to engineering challenges.
- Develop LLM workflows for data parsing, summarization, extraction, classification, and structured outputs.
- Design and implement RAG solutions grounded in trusted engineering documents, datasets, and approved knowledge sources.
- Build AI-agent and LLM harness workflows with task routing, tool calling, workflow orchestration, evaluation, and guardrails.
- Develop or integrate custom tools for AI interaction with engineering and technical data sources.
- Collaborate with signal integrity, product development, testing, manufacturing, and operations teams on AI automation and decision support.
- Translate technical requirements into reliable, reusable AI workflows and prototypes.
- Document workflows, assumptions, validation approaches, limitations, and recommended next steps.
- Evaluate AI-generated results, identify risks and limitations, and recommend improvements.
Requirements:
- Bachelor's degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related technical discipline; master's degree is a plus.
- Strong hands-on Python experience for AI/ML development, data processing, model training, validation, and automation.
- Understanding of machine learning and deep learning, including neural networks, CNNs, and LSTM/recurrent architectures.
- Experience with PyTorch, TensorFlow, or equivalent AI/ML frameworks.
- Understanding of GPU-enabled AI/ML development and CUDA, particularly in NVIDIA environments.
- Practical knowledge of Large Language Models and experience with open-source and/or commercial AI models.
- Experience with Ollama, LM Studio, llama.cpp, or equivalent local LLM/model-serving tools.
- Experience with Hugging Face, LangChain, or similar AI/LLM frameworks.
- Strong understanding and implementation experience with Retrieval-Augmented Generation (RAG).
- 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 focused on validating AI outputs and understanding model limitations.
- Excellent communication skills and ability to explain AI concepts and technical tradeoffs to engineering stakeholders.
- Ability to work independently, learn quickly, and collaborate within a global technical organization.
- Nice-to-have: experience with engineering, signal-integrity, measurement, simulation, test, product-development datasets, AWS AI/data environments, Jupyter notebooks, LLM fine-tuning, model serving, GPU optimization, and related engineering environments.
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