AI Cloud Engineer (AWS) – MLOps (Remote)

About the role

We are seeking an experienced AI Cloud Engineer specialized in AWS and MLOps to help scale Tenjah, an EdTech platform built on Retrieval‑Augmented Generation (RAG) architecture. The mission is part‑time or full‑time, remote, and focused on structuring, securing, and optimizing the cloud infrastructure that powers our RAG pipelines and large language model services.

Key responsibilities

• Design, deploy and manage a scalable AWS environment (EC2/ECS/Fargate, Lambda, S3, RDS, VPC, IAM, CloudWatch).
• Implement CI/CD pipelines for backend services and AI components using Docker and GitHub Actions.
• Build MLOps pipelines for model versioning, automated training/fine‑tuning, and regression testing of RAG responses.
• Integrate and optimize a vector database (Pinecone, Weaviate, or pgvector) for the retrieval layer.
• Set up monitoring, logging, and alerting with CloudWatch, Grafana or Prometheus.
• Ensure security best practices, including secrets management, least‑privilege IAM, and data encryption.
• Document architecture and deployment procedures to support project continuity.
• Collaborate with the founding team to align technical evolution with the product roadmap.

Required profile

• Proven experience in AWS cloud engineering, preferably with an AWS Solutions Architect or DevOps Engineer certification.
• Hands‑on MLOps experience deploying and monitoring LLM/RAG pipelines in production.
• Strong autonomy, rigor, and ability to produce clear documentation.

Required skills

• AWS (EC2, ECS/Fargate, Lambda, S3, RDS, VPC, IAM, CloudWatch)
• Docker
• CI/CD (GitHub Actions or equivalent)
• Python
• FastAPI
• LangChain
• Vector databases: Pinecone, Weaviate, pgvector
• LLM APIs (Gemini, Groq, OpenRouter)
• Monitoring tools: Grafana, Prometheus