GenAI Engineer for Enterprise AI Platforms

Papigen; United States; Remote; USD 140,000 - 190,000; Full time

Job summary

Papigen seeks an experienced GenAI Engineer to design, build, and operationalize enterprise-grade AI solutions using LLMs, RAG, embeddings, and AI agents. You will architect scalable platforms and integrate enterprise knowledge sources across cloud environments.

You will deploy AI workloads on Azure/AWS, implement Responsible AI controls, and collaborate with security, architecture, and business teams to deliver production-ready capabilities.

Qualifications

• Bachelor’s degree in CS, AI, data science or related field.
• Hands-on experience building enterprise GenAI solutions.
• Experience with RAG pipelines, embeddings, grounding, and evaluation.
• Knowledge of vector databases and search platforms (Azure AI Search, Redis, PostgreSQL).
• Strong prompt engineering and AI evaluation skills.

Responsibilities

• Architect end-to-end GenAI solutions using LLMs and RAG.
• Design AI agents and orchestration workflows for enterprise use cases.
• Develop document ingestion pipelines including OCR and metadata enrichment.
• Implement grounding, citation validation, and response verification.
• Design APIs, SDKs, and reusable integration frameworks.
• Deploy AI workloads on Azure and AWS and monitor operations.
• Collaborate with security, architecture, and business teams.

Skills

GenAI design

RAG pipelines

Chunking strategies

Embeddings

Vector databases

Azure OpenAI

Claude

Gemini

Prompt engineering

CI/CD pipelines

Kubernetes

Cloud platforms

Education

Bachelor's degree in Computer Science or related

Tools

Workato

Azure

AWS

DigitalNet

Azure AI Search

Redis

PostgreSQL

Job description

Papigen seeks an experienced GenAI Engineer to design, build, and operationalize enterprise-grade AI solutions using LLMs, RAG, embeddings, and AI agents. You will architect scalable platforms and integrate enterprise knowledge sources across cloud environments.

You will deploy AI workloads on Azure/AWS, implement Responsible AI controls, and collaborate with security, architecture, and business teams to deliver production-ready capabilities.