GE Vernova

Staff AI Software Engineer

In short

This role leads architecture, development, and the full lifecycle of AI Agents and MCP (Model Context Protocol) servers for an enterprise agentic AI platform. It involves building LLM-integrated applications, implementing RAG patterns, and driving end-to-end feature delivery with coding agents, while leading technical reviews and guiding external contractors. It requires 6+ years of programming experience (Python preferred), proven production LLM/AI shipping experience, strong cloud (AWS) and containerization skills, and technical leadership experience.

Job Description Summary
As a Staff AI Engineer, you will lead the architecture, development, and end-to-end lifecycle of our AI Agents and the underlying infrastructure that powers it. You will design and deploy AI agents and robust MCP (Model Context Protocol) servers that standardize data connectivity, skills, and tools. You will also own the full stack, from design and clean code implementation to rigorous testing and production support. By balancing high-level system strategy with hands-on technical execution, you will ensure our agentic workflows are scalable, secure, and seamlessly integrated into the enterprise ecosystem.
You will partner with internal teams and external contractors to lead technical reviews, integrations, and delivery execution. You will deliver using Coding Agents, establish testing approaches for prompts and model performance, and implement observability for AI features. We value autonomy, continuous learning, and clear business impact. If you are excited by this work but unsure whether you meet every requirement, we encourage you to apply.
Job Description
Roles and Responsibilities
•Collaborate with cross-functional teams (product, design, operations, infrastructure) to build AI-native application experiences
•Build AI Agents, deploy MCP Servers, Tools and integrate LLM / generative AI capabilities into products, including structured prompting, memory management and context management
•Implement retrieval-augmented generation (RAG) patterns (e.g., embeddings + retrieval) and contribute to context-aware and personalized user experiences
•Drive end-to-end execution of medium-to-large features with independence, contribute to technical direction within the team
•Deliver using Coding Agents and Eval Driven Development
Required Qualifications
•Bachelor's degree in computer science, Computer Engineering, or related technical field
•6+ years of experience using at least one general purpose programming language (Python [preferred], Java, or Go) and front-end experience with JavaScript/TypeScript
•Proven track record shipping production applications that incorporate LLMs, MCPs or AI services at scale
•Demonstrated experience leading technical teams and providing guidance to external contractors or vendors
•Strong experience with cloud platforms (i.e. AWS) and advanced containerization (Docker, Kubernetes)
•Experience with technical oversight, code review processes, and quality assurance methodologies
Preferred Qualifications
•Experience with Strands, ADK or Agent development frameworks
•Experience with multiple LLM providers and advanced AI orchestration frameworks (e.g. Amazon Bedrock AgentCore, LangChain)
•Experience with modern front-end frameworks (React, Vue, or Angular)
•Experience with vector databases (e.g. pgvector) and embedding model optimization
•Experience with enterprise RAG architectures, semantic search optimization, and conversation memory management at scale
•Experience building and scaling AI-powered features for 100k+ users
Additional Information
Relocation Assistance Provided: Yes

Stop applying.
Get referred.

$10 in free credits

In just 60 seconds, fully on autopilot — Eraldt's AI agents read your CV, map the roles you fit, find live vacancies and the people who can refer you, write personal emails, and get you referred.

This isn't an application: your CV doesn't go to the company, and we don't promise a referral for this exact role.

Discover who else lands interviews on autopilot