AI Engineer - Generative AI and Agents

Azumo

Brasil

Remote

BRL 653,000 - 870,000

Full time

7 hours ago

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Benefits offered by this job

Remote-first

PTO

US Holidays

AI training

Career mentoring

Profit sharing

US remuneration

Job summary

Azumo is hiring an AI Engineer to own production behavior for retrieval pipelines, tool-using agents, and evaluation harnesses. This fully remote role spans Latin America and aligns to client working days. You will work across four engineering lanes, with emphasis on dependable, measurable production AI systems.

The role requires hands-on backend and LLM experience, plus strong English communication. Azumo has shipped production AI since 2016 and collaborates with clients across major tech

Qualifications

4+ years building and shipping production software with modern backend tech.
Production experience with LLM-based systems and tool calling.
Hands-on with vector and retrieval infrastructure (pgvector, Pinecone, Qdrant, FAISS, Azure AI Search).
Experience with agent frameworks and tooling via LangGraph, LangChain, CrewAI or MCP.
Built evaluation suites for LLM systems with Langfuse or LangSmith.
Cloud deployment experience with Docker, CI/CD, IaC (GitHub Actions, Terraform or Bicep).
Strong written/spoken English (C1+).
Bachelor’s degree or equivalent professional experience.

Responsibilities

Build retrieval systems including chunking, embeddings, and hybrid search.
Develop stateful, tool-using agent workflows and MSP servers.
Design evaluation sets and model-as-judge scoring with regression tracking.
Ensure reliability, security, and guardrails in production AI.
Deploy production software on Azure or AWS with observability and cost awareness.

Skills

4+ years exp

Production software

Python

LLM systems

Vector retrieval infra

English proficiency

Backend fundamentals

CI/CD

Education

Bachelor's in CS/DS or related

Tools

pgvector

Pinecone

Qdrant

FAISS

Azure AI Search

LangGraph

LangChain

CrewAI

MCP

Langfuse

Ragas

LangSmith

Docker

GitHub Actions

Terraform

Bicep

Azure

AWS

Job description

Azumo builds and operates production AI systems for companies ranging from seed‑stage startups to Meta. We are hiring an AI Engineer to own what those systems do once they are live: retrieval pipelines, tool‑using agents, evaluation harnesses, and the guardrails that keep them dependable in front of real users. The role is fully remote across Latin America, aligned to your client's working day.

You will not be building demos. Azumo has shipped production AI since 2016, and the work here starts where the prototype ends, making a system reliable, measurable, and affordable enough to put in front of customers.

Where this role sits

Azumo's engineering organization is built around four lanes. The Data Engineer lane owns pipelines, storage, and the retrieval layer. The Data Scientist lane owns the question and the method. The Software Engineer lane owns AI‑augmented product delivery. This role is the AI Engineer lane, and it owns production behavior.

One question places the boundary: when the output is wrong, whose problem is it? "The method was inappropriate" is a Data Scientist question. "The system did the wrong thing with an appropriate method" is yours.

Not quite your profile? Check our other openings:

If you build the pipelines and retrieval layer models depend on — Data Engineer
If you decide what to measure and which method answers it — Data Scientist
If you ship product software with agents in your toolchain — AI‑Augmented Software Engineer
If you've done all of the above and answered to the client directly — Forward Deployed Engineer

What You Will Build

Retrieval systems. Chunking and embedding pipelines, hybrid search, reranking, and evaluation of retrieval quality, built on pgvector, Pinecone, Qdrant, FAISS, or Azure AI Search
Agentic workflows. Stateful multi‑step execution, tool calling, MCP servers, structured output enforcement, context‑window management, deterministic fallbacks, and human‑in‑the‑loop gates for the decisions that need one
Evaluation. Test sets that reflect the decision the system is actually making, model‑as‑judge scoring, regression tracking across prompt and model changes, and honest error analysis. If a change made the system better, you should be able to prove it
Reliability and safety. Prompt‑injection defense, output validation, guardrails, PII handling, and graceful degradation when a model or tool call fails
Production operation. Containerized deployment on Azure or AWS, CI/CD, observability, and explicit latency, cost, and token budgets that you own rather than discover after the invoice
Work inside the client's environment. Their repositories, their standups, sometimes their customer calls. Azumo is SOC 2 certified, client code stays in client repositories, and some engagements carry additional requirements such as HIPAA

How We Work

Our engineers build with AI every day. Claude Code, Codex, and similar tools are part of the standard toolchain here, not an experiment. We run an automated audit across the whole codebase on day one and every day after, grading security, cost, and architecture findings by severity with the exact file and line, so a small team can move quickly without quality drifting. We stay vendor‑neutral across OpenAI, Anthropic, and open‑weight models, and we run Valkyrie, our own production layer, when a single interface to any model is the right call.

About Azumo

Azumo is a San Francisco based software development company that has been building intelligent applications since 2016. We provide nearshore AI engineering teams to organizations that need production AI faster than they can hire for it: as an embedded engineering team, as AI staff augmentation alongside an existing team, or as a full project build. Our engineers work from Latin America, aligned to United States time zones, and have delivered for Twitter, Meta, Discovery Channel, Omnicom, UnitedHealth, and CENTEGIX.

We hire for seniority and test for it before anyone joins a client team. We support engineers in going deep on the modern AI stack, and we give time back to open‑source work, community teaching, and philanthropy.

Requirements

Basic qualifications

4+ years building and shipping production software, with a modern backend language (e.g., Python) as your primary focus, plus the engineering fundamentals that go with it: testing, code review, CI/CD, Git, containers, API design, and async programming
Demonstrable production experience with LLM‑based systems: retrieval‑augmented generation, function and tool calling, structured output enforcement, and prompt design as an engineering discipline rather than trial and error
Hands‑on work with vector and retrieval infrastructure such as pgvector, Pinecone, Qdrant, FAISS, or Azure AI Search, including the retrieval‑quality problems that come with it
Experience with agent frameworks and tooling: LangGraph, LangChain, CrewAI, the Model Context Protocol (MCP), or native Python execution loops. We care that you have shipped a stateful, tool‑using system, not which framework you used
You have built an evaluation suite for an LLM system. Test‑set design, model‑as‑judge or equivalent scoring, and regression tracking with Langfuse, Ragas, LangSmith, or something you wrote yourself. This is the requirement we screen hardest on
Cloud deployment experience, Azure preferred and AWS acceptable, with Docker, CI/CD pipelines, and infrastructure as code (GitHub Actions, Terraform, or Bicep)
Working discipline around latency, token cost, and throughput. You can explain what a feature costs to run and what you did about it
Active use of AI‑assisted coding tools such as Claude Code, Cursor, or GitHub Copilot in real delivery work
Clear written and spoken English, C1 or above, and the confidence to explain a technical trade‑off directly to a client
Bachelor's degree in Computer Science, Data Science, or a related field, or equivalent professional experience

Preferred Qualifications

Fine‑tuning and adaptation of open‑weight models: LoRA, QLoRA, PEFT, and a clear view of when fine‑tuning is the wrong answer
Self‑hosted or open‑weight inference and serving, and the cost and latency trade‑offs against hosted APIs
Multimodal systems covering vision, speech, or document understanding alongside text
Security work specific to LLM systems: prompt‑injection testing, red‑teaming, and output sanitization
Delivery under a compliance regime such as SOC 2 or HIPAA
Streaming, real‑time, or high‑throughput inference workloads
Contributions to open‑source AI libraries, published technical writing, or active participation in the AI engineering community

Benefits