Remote GenAI Engineer — Gemini Enterprise on GCP

Nelnet

Helena (MT)

Hybrid

USD 130,000 - 150,000

Full time

5 days ago
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Benefits offered by this job

Hybrid work environment
Medical, dental, vision benefits
401K / student loan repayment
Life insurance & AD&D
Employee stock purchase program
Tuition reimbursement
Disability insurance
Wellness program
Visa-free and remote-friendly

Job summary

Nelnet is seeking a GenAI Platform Engineer to own the technical build and deployment of agentic AI solutions on the Gemini Enterprise Platform. You will translate backlog items into working solutions and collaborate with data and deployment engineers to ensure quality and delivery.

The role emphasizes RAG pipelines, model integration, and enterprise data access. A strong emphasis is placed on production-grade, scalable integrations and a commitment to security and FERPA considerations.

Qualifications

  • Hands-on experience building GenAI/agentic AI solutions in production.
  • Direct experience with Gemini Enterprise Platform or Vertex AI preferred; other GenAI platforms considered.
  • Experience with LLMs, prompting, fine-tuning, or grounding techniques.
  • Familiarity with cloud data, compute, and access-control services for AI workloads.
  • Software engineering fundamentals in Python and relevant SDKs.

Responsibilities

  • Design, build, and deploy agentic assistants and GenAI applications on the Gemini Enterprise Platform.
  • Implement RAG pipelines and grounding using platform capabilities.
  • Integrate Agent2Agent (A2A) protocol and multi-agent orchestration patterns.

Skills

GenAI platforms
LLM / prompt engineering
RAG pipelines
Multi-agent orchestration
Python / SDKs
Google Cloud / Gemini

Tools

Gemini Enterprise Platform
Vertex AI
Google Cloud
BigQuery
Snowflake
Cloud Run / GKE
IAM
Model Garden
A2A protocol

Job description

Nelnet is seeking a GenAI Platform Engineer to own the technical build and deployment of agentic AI solutions on the Gemini Enterprise Platform. You will translate backlog items into working solutions and collaborate with data and deployment engineers to ensure quality and delivery.

The role emphasizes RAG pipelines, model integration, and enterprise data access. A strong emphasis is placed on production-grade, scalable integrations and a commitment to security and FERPA considerations.

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