Remote AI Engineer: LLMs, RAG Apps & Cloud Deployments

Everestek Technosoft Solutions; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Remote work

Job summary

Everestek Technosoft Solutions is seeking an AI Engineer to design and deploy scalable AI solutions using Python, FastAPI, and cloud platforms. You will build LLM-powered applications, RAG pipelines, and AI microservices for insurance workflows such as underwriting, claims, and policy servicing.

You will work on integrating LLM APIs, embeddings, and retrieval systems, leveraging LangChain, vector databases, and production-grade observability and security practices.

Qualifications

• Proficient in Python with backend frameworks (FastAPI/Flask).
• Hands-on ML/DL with PyTorch or TensorFlow and Transformers.
• Experience integrating LLM APIs, embeddings, RAG, retrieval systems.
• Familiar with LangChain and AI orchestration.
• Experience with vector databases (Pinecone/Weaviate/FAISS).
• Deploying AI apps on AWS/Azure/GCP.
• Knowledge of model evaluation, observability, latency and reliability.
• Knowledge of secure AI, data privacy and auditable design.

Responsibilities

• Develop and deploy scalable AI solutions.
• Build LLM-powered applications, RAG pipelines and AI microservices.
• Address insurance processes like underwriting, claims and policy servicing.
• Implement secure, observable, and compliant AI systems.

Skills

Python

FastAPI

Flask

PyTorch

TensorFlow

Hugging Face Transformers

LangChain

AWS

Azure

GCP

Docker

Kubernetes

LLM APIs

Embeddings

RAG

Vector databases

CI/CD

Security & Compliance

Tools

Pinecone

Weaviate

FAISS

Job description

Everestek Technosoft Solutions is seeking an AI Engineer to design and deploy scalable AI solutions using Python, FastAPI, and cloud platforms. You will build LLM-powered applications, RAG pipelines, and AI microservices for insurance workflows such as underwriting, claims, and policy servicing.

You will work on integrating LLM APIs, embeddings, and retrieval systems, leveraging LangChain, vector databases, and production-grade observability and security practices.