Principal GenAI Engineer – Backend / Fullstack

Globespan; Washington (District of Columbia); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Turing is seeking a Principal GenAI Engineer to lead enterprise-scale AI implementations for Fortune 500 clients. You will design and build scalable, production-grade GenAI systems powered by LLMs, RAG, and agent-based architectures in a hybrid Washington DC setting.

The role sits at the intersection of backend/fullstack engineering and applied AI, building reliable systems that tightly integrate LLM capabilities into real-world applications for large organizations.

Qualifications

• 8–14 years of software engineering experience.
• 2+ years with LLMs (RAG, agents, prompt engineering).
• Experience building production-grade distributed systems.
• Proficiency in Python.
• Experience with SQL and NoSQL databases.
• Experience deploying systems on AWS / Azure / GCP and APIs/microservices.

Responsibilities

• Design and build scalable GenAI applications using LLMs and RAG pipelines.
• Develop and optimize backend services and APIs for AI-powered systems.
• Build and deploy agent-based workflows and orchestration systems.
• Integrate LLMs into real-world enterprise applications.
• Ensure performance, scalability, and reliability of AI systems in production.
• Collaborate with product, data, and engineering teams to deliver end-to-end solutions.
• Implement monitoring, evaluation, and guardrails for GenAI systems.

Skills

Software engineering

LLMs experience

Python

APIs and microservices

System design

Cloud deployment (AWS/Azure/GCP)

Tools

LangChain

LangGraph

Pinecone

Weaviate

FAISS

AWS

Azure

GCP

React

Next.js

Job description

Washington DC – Hybrid

Full time

Experience Level: Principal (10-14 years)

About the Role

Turing is hiring a Principal GenAI Engineer to lead enterprise-scale AI implementations for Fortune 500 clients. This role focuses on designing and building scalable, production-grade GenAI systems powered by LLMs, Retrieval-Augmented Generation (RAG), and agent-based architectures.

You will work at the intersection of backend/fullstack engineering and applied AI, building reliable, high-performance systems that integrate LLM capabilities into real-world applications.

What We’re Looking For

• 8-14 years of experience in software engineering (backend or fullstack)
• 2+ years of hands-on experience with LLMs (RAG, agents, prompt engineering)
• Strong experience building production-grade distributed systems
• Proficiency in Python
• Strong experience with SQL & NoSQL databases
• Hands-on experience with LangChain, LangGraph, or similar frameworks
• Experience deploying systems on AWS / Azure / GCP
• Strong understanding of APIs, microservices, and system design

Key Responsibilities

• Design and build scalable GenAI applications using LLMs and RAG pipelines
• Develop and optimize backend services and APIs for AI-powered systems
• Build and deploy agent-based workflows and orchestration systems
• Integrate LLMs into real-world enterprise applications
• Ensure performance, scalability, and reliability of AI systems in production
• Collaborate with product, data, and engineering teams to deliver end-to-end solutions
• Implement monitoring, evaluation, and guardrails for GenAI systems

Good to Have

• Experience with vector databases (Pinecone, Weaviate, FAISS, etc.)
• Exposure to frontend frameworks (React, Next.js) for fullstack roles
• Familiarity with CI/CD pipelines and DevOps practices
• Understanding of model evaluation, fine-tuning, or LLMOps
• Experience building multi-tenant or enterprise SaaS platforms