Senior Python AI Engineer — LLMs, ML, Cloud & MLOps

Aether Biomedical; United States; On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Vacation days 26

Health insurance

Mental health support

Learning platforms access

Hybrid work option

Certification reimbursement

Job summary

Aether Biomedical is seeking a Senior Python Engineer to design, develop, and deploy AI and ML driven solutions at scale. You will build models, embeddings, and RAG pipelines, and productionize ML services across Azure, AWS, and GCP.

Join a team focused on enterprise-ready AI, data pipelines, and observability, with hybrid work options and strong learning opportunities through internal platforms.

Qualifications

• Strong proficiency in Python with ML libraries and frameworks.
• Hands-on experience with LLMs, embeddings, transformers, and RAG pipelines.
• Experience deploying ML models on AWS, Azure, or GCP.
• Experience with vector databases and related search services.
• Familiarity with Docker, Kubernetes, and CI/CD for ML.
• Background building scalable ML services and MLOps tooling.

Responsibilities

• Design, build, and deploy ML and generative AI models, incl. LLMs and RAG.
• Develop scalable AI services and microservices on cloud-native tech.
• Optimize models for performance, cost, and accuracy.
• Create data pipelines for training, validation, and inference.
• Collaborate with data engineering on data ingestion and governance.
• Implement CI/CD for ML models and MLOps workflows.
• Monitor drift and retraining needs; manage model lifecycle.
• Integrate AI systems with enterprise apps and cloud platforms.

Skills

Python

NumPy

Pandas

PyTorch

TensorFlow

Transformers

LLMs

MLOps

Docker

Kubernetes

Cloud platforms

Vector databases

Tools

LangChain

LangGraph

PyTorch/TensorFlow tooling

SageMaker

Databricks

FAISS

Pinecone

Weaviate

Job description

Aether Biomedical is seeking a Senior Python Engineer to design, develop, and deploy AI and ML driven solutions at scale. You will build models, embeddings, and RAG pipelines, and productionize ML services across Azure, AWS, and GCP.

Join a team focused on enterprise-ready AI, data pipelines, and observability, with hybrid work options and strong learning opportunities through internal platforms.