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Full Stack AI Engineer

Relanto, Inc.
San Jose, CA, United States
12 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source
www.dice.com

Tech stack

Amazon Web Services Elasticsearch Python (Programming Language) MongoDB Node.Js Redis Next.js TypeScript ReactJS Large Language Models Backend Fastapi
+3 more
Kubernetes Front End Software Development Docker

Job description

A full stack engineer who can work across our Python agent layer, Node.js backend, and Next.js frontend. Someone who has built LLM applications in production, not just prototypes.

Requirements

  • Strong Python - async, FastAPI or similar
  • Strong TypeScript/Node
  • Next.js and React, including streaming UI
  • Experience building LLM-backed apps with tool calling and multi-step agents
  • Understanding of RAG and retrieval quality
  • Docker, Kubernetes, AWS MongoDB, Redis

Nice to have:

  • LangGraph or LangChain
  • AWS Bedrock, or an LLM gateway like Portkey
  • Elasticsearch
  • OpenFGA or similar fine-grained authorization
  • Experience evaluating non-deterministic systems

Experience level:

  • 5+ years engineering. Senior IC. Comfortable owning a feature end to end and working without a fully specified spec.

Our stack:

  • Python + LangGraph for agent orchestration on EKS.
  • Node.js backend. Next.js frontend. AWS Bedrock via Portkey.
  • Elasticsearch for RAG.
  • OpenFGA, Redis, MongoDB.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.dice.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

Explore playlists

3:55 min

Demonstrating semantic routing thresholds with the Redis vector library

2:07 min

Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · WWC 2025

2:01 min

Migrating existing applications from MongoDB to Postgres

Nikita Shamgunov Nikita Shamgunov · WWC 2024

3:25 min

Constructing scalable AI solutions using LangChain and LangGraph

Julián Duque Julián Duque · WWC 2025

3:42 min

Comparing in-memory and Redis storage for cache scalability

Simone Sanfratello · WWC 2022

1:02 min

Technology stack for building retrieval-augmented generation applications

Ashish Sharma · LIVE

Videos

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Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis) thumbnail
34:39
12 months ago

Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)

Docker Compose: Rediscovered thumbnail
28:40
Europe 2026 Virtual • about 1 month ago

Docker Compose: Rediscovered

Matthieu Vincent

40 Minutes to Build a Serverless COVID-19 REST and GraphQL APIs thumbnail
47:16
WWC 2021 • about 5 years ago

40 Minutes to Build a Serverless COVID-19 REST and GraphQL APIs

Maxime Beugnet

One AI API to Power Them All thumbnail
30:48
WWC 2025 • 12 months ago

One AI API to Power Them All

Roberto Carratalá

Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps thumbnail
28:57
WWC 2024 • almost 2 years ago

Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps

David Leconte, Michel de Ru

Accelerating Authentication Architecture: Taking Passwordless to the Next Level thumbnail
49:52
LIVE • almost 3 years ago

Accelerating Authentication Architecture: Taking Passwordless to the Next Level

Yedidya Schwartz

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