AI Operations Architect & AWS Engineer — Life Sciences

Norstella Group; United States; Remote; USD 180,000 - 230,000; Full time

Job summary

Norstella is seeking an AI Operations Architect & AWS Engineer to design, deploy, and operate production-grade LLM systems on AWS infrastructure. You will build agentic AI pipelines powering large-scale clinical data extraction from real-world notes, accelerating drug development and patient access decisions.

The role sits at the intersection of ML infrastructure, cloud engineering, and applied NLP, owning the backbone that turns LLM capabilities into reliable data products while ensuring

Qualifications

• Master's degree in Computer Science, Data Science, or related field.
• 5+ years in production NLP/ML systems on AWS.
• Experience deploying open-weight LLMs (Llama, Gemma, Qwen) at scale.
• Expert Python and LLM orchestration tooling (LangChain/LangGraph).
• Experience with AWS ML services and IaC (Terraform, CloudFormation, CDK).

Responsibilities

• Design and maintain AWS architectures for LLM inference and fine-tuning.
• Build and operate agentic AI pipelines with multi-step LLM orchestration.
• Deploy and manage open-weight LLMs in production with optimized serving.
• Implement evaluation, drift detection, and QC for LLM-based extraction.
• Develop CI/CD pipelines for prompt/versioning and model updates.
• Ensure HIPAA/PHI compliance in LLM deployment and data processing.

Skills

NLP/ML systems

AWS

Python

LLM orchestration

LangChain/LangGraph

Cloud infrastructure

Communication skills

Education

Master's degree in Computer Science/Data Science

Tools

vLLM

TGI

Terraform

CloudFormation

CDK

SageMaker

EC2 GPU

S3

LangChain

Job description

Norstella is seeking an AI Operations Architect & AWS Engineer to design, deploy, and operate production-grade LLM systems on AWS infrastructure. You will build agentic AI pipelines powering large-scale clinical data extraction from real-world notes, accelerating drug development and patient access decisions.

The role sits at the intersection of ML infrastructure, cloud engineering, and applied NLP, owning the backbone that turns LLM capabilities into reliable data products while ensuring