Senior LLM Engineer: Azure AI & Tooling Lead

Flint Hills Resources; Austin, Fremont, Lisle (TX, CA, IL); On-site; USD 195,000 - 255,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Flexible spending accounts

Health savings accounts

Life insurance

Retirement plan

Paid vacation

Educational assistance

Parental leave

Job summary

Molex is seeking a Senior LLM Engineer in Austin to fine-tune and deploy LLMs on Azure OpenAI / Azure AI Foundry for our engineering design platform. Translate natural-language design intent into structured, engineering-valid outputs.

You will work with ML engineers, data scientists, and MLOps to keep training and inference workloads reliable, secure, and cost-efficient while integrating with internal tools and data sources.

Qualifications

• 8+ years of ML/AI engineering experience with hands-on LLM work beyond API usage.
• Experience fine-tuning models (LoRA/QLoRA/PEFT or full fine-tuning).
• Experience with alignment techniques (RLHF/RLAIF/DPO).
• Strong Python and PyTorch; deep transformer knowledge.
• Hands-on with RAG pipelines, embeddings, and tool-use LLM systems.
• Familiarity with Azure OpenAI or similar cloud LLM platforms.

Responsibilities

• Fine-tune foundation models (LoRA/QLoRA, RLHF, instruction tuning) for domain-specific tasks.
• Build agentic workflows connecting LLMs to internal tools and data sources.
• Design evaluation harnesses for factuality and guardrails.
• Build RAG pipelines and tool-use systems on Azure OpenAI models.
• Deploy and optimize inference on Azure Kubernetes Service for cost efficiency.

Skills

LLM/Foundation model work

Python

PyTorch

LoRA/QLoRA/PEFT

Alignment techniques

Azure OpenAI / Foundry

RAG pipelines

Multi-GPU training

Tools

Azure OpenAI Service

Azure AI Foundry

DeepSpeed

vLLM/TensorRT-LLM

Job description

Molex is seeking a Senior LLM Engineer in Austin to fine-tune and deploy LLMs on Azure OpenAI / Azure AI Foundry for our engineering design platform. Translate natural-language design intent into structured, engineering-valid outputs.

You will work with ML engineers, data scientists, and MLOps to keep training and inference workloads reliable, secure, and cost-efficient while integrating with internal tools and data sources.