Senior AI Platform Architect - RAG, LLM & Microservices

LS3002 GE Vernova Operations LLC; Cambridge (MA); On-site; USD 128,000 - 213,000; Full time

Benefits offered by this job

Healthcare benefits

Relocation assistance

401(k) with company match

Job summary

GE Vernova is seeking a Senior Staff AI Engineer to lead enterprise full stack and AI platform architecture at scale. You will design and ship microservices and REST APIs, build RAG and multimodal LLM features, and set standards for AI-assisted development.

You will own technical decisions end-to-end, balancing reliability, performance, and cloud cost for AI systems, while mentoring engineers, running reviews, and driving Agile practices with observability for AI features.

Qualifications

• Bachelor’s degree in CS, Computer Engineering, or related field.
• 8+ years building production apps with at least one major language and front-end experience.
• Proven track record shipping LLM/AI services at scale.
• Experience leading technical teams and guiding external contractors.
• Strong cloud experience (AWS/GCP/Azure) and advanced containerization (Docker, Kubernetes).
• Experience with oversight, code reviews, and QA methodologies.

Responsibilities

• Identify and deliver reusable AI components for forward-deployed engineers.
• Ship resilient REST APIs and microservices.
• Build RAG and multimodal LLM features.
• Design agentic workflows for autonomous tasks.
• Mentor engineers and guide contractors/partners.

Skills

Python

Java

Go

JS/TS

LLMs

Cloud platforms

Docker

Kubernetes

Education

Bachelor’s degree in CS/Engineering

Tools

Docker

Kubernetes

React

LangChain

LlamaIndex

Milvus

Pinecone

Weaviate

Job description

GE Vernova is seeking a Senior Staff AI Engineer to lead enterprise full stack and AI platform architecture at scale. You will design and ship microservices and REST APIs, build RAG and multimodal LLM features, and set standards for AI-assisted development.

You will own technical decisions end-to-end, balancing reliability, performance, and cloud cost for AI systems, while mentoring engineers, running reviews, and driving Agile practices with observability for AI features.