Senior LLM Engineer — Build AI Tools on Azure OpenAI

Molex; Fremont (CA); On-site; USD 195,000 - 255,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Paid time off

Educational assistance

Job summary

Molex is seeking a Senior LLM Engineer to fine-tune and deploy LLMs on Azure OpenAI/Azure AI Foundry, powering generative capabilities in our engineering design platform. You will translate natural-language design intent into structured, engineering-valid outputs.

Join a platform team of ML engineers, data scientists, and MLOps, optimizing training, inference, and cost across enterprise-scale deployments.

Qualifications

• 8+ years of ML/AI engineering experience with hands-on LLM work.
• Experience fine-tuning models (LoRA/QLoRA/PEFT or full fine-tuning).
• Experience with alignment techniques (RLHF, RLAIF, DPO, or instruction tuning).
• Strong Python and PyTorch; understanding transformer internals.
• Hands-on with RAG pipelines, embeddings, and tool-use LLM systems.
• Familiarity with Azure OpenAI / Azure AI Foundry or equivalent.

Responsibilities

• Fine-tune foundation models (LoRA/QLoRA, RLHF/DPO) for domain tasks.
• Build agentic workflows connecting LLMs to internal tools and data.
• Design evaluation harnesses for factuality; own safety guardrails.
• Build RAG pipelines and function-calling systems atop Azure OpenAI models.
• Deploy and optimize inference on AKS for cost-efficient serving.

Skills

8+ years ML/AI engineering

LLM/foundation model work

LoRA/QLoRA/PEFT fine-tuning

RLHF/RLAIF/DPO/instruction tuning

Python

PyTorch

RAG pipelines / embeddings / vector 검색

Azure OpenAI / Azure AI Foundry

Distributed training (multi-GPU/multi‑

Tools

DeepSpeed

TensorRT-LLM

Azure Kubernetes Service

Job description

Molex is seeking a Senior LLM Engineer to fine-tune and deploy LLMs on Azure OpenAI/Azure AI Foundry, powering generative capabilities in our engineering design platform. You will translate natural-language design intent into structured, engineering-valid outputs.

Join a platform team of ML engineers, data scientists, and MLOps, optimizing training, inference, and cost across enterprise-scale deployments.