Production AI Engineer, Healthcare Ops & LLMs

Autonomize AI; Austin (TX); On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Employer-paid health insurance

401(k) retirement plan

Career growth opportunities

Job summary

Autonomize AI is seeking a hands-on AI Engineer to ship real-world AI-native systems behind healthcare agents and copilots. You will blend LLMs, vision models, and traditional ML across structured and unstructured data to drive decisions in high-stakes workflows.

You will build production pipelines, extract data from medical documents, fine-tune models, and deploy solutions with a focus on performance, safety, and explainability.

Qualifications

• 2+ years experience working in applied ML and LLMs.
• Strong Python skills with PyTorch and Hugging Face Transformers.
• Familiarity with embeddings, vector search, retrieval pipelines, and prompt engineering.
• Experience deploying models to production and MLOps tooling (MLflow, Docker, Kubernetes).
• BS/MS in Computer Science, Engineering, Data Science, or related field, or equivalent experience.

Responsibilities

• Build production AI pipelines combining LLMs and classical ML (RAG, extraction, scoring, summarization, classification).
• Extract from real-world documents using VLM- and OCR-based pipelines.
• Fine-tune/adapt models (SFT, LoRA) on open-source and proprietary models.
• Implement retrieval and agents for production use cases.
• Monitor model quality, latency, cost, explainability, and safety in production.
• Collaborate with ML engineers, product and domain experts.

Skills

Applied ML

LLMs

Python

PyTorch

Hugging Face

LangChain

LangGraph

LlamaIndex

Embeddings

NLP

Education

BS/MS in Computer Science, Engineering, Data Science, or related field

Tools

Docker

Kubernetes

MLflow

LangChain

LangGraph

LlamaIndex

Job description

Autonomize AI is seeking a hands-on AI Engineer to ship real-world AI-native systems behind healthcare agents and copilots. You will blend LLMs, vision models, and traditional ML across structured and unstructured data to drive decisions in high-stakes workflows.

You will build production pipelines, extract data from medical documents, fine-tune models, and deploy solutions with a focus on performance, safety, and explainability.