Data Engineer - Oracle to PostgreSQL Re-Platform ( 102-08SENG-02 )

Cloudary; Acre; Presencial; BRL 240 000 - 360 000; Tempo integral

Resumo da oferta

Cloudary is seeking an experienced software engineer to lead the end-to-end delivery of a production-grade LLM agent, from requirements to deployment, and to defend architectural decisions directly with the client. You will build and harden agent orchestration, integrate tools across MCP, and run models on AWS Bedrock with Bedrock AgentCore, ensuring guardrails, memory management, and regional residency.

You will also develop evaluation harnesses, instrument observability, and pursue cost and

Qualificações

• 5+ years in software engineering, with at least 2 genuinely at a senior level.
• Strong Python in production.
• Comfort picking up TypeScript or Go when a project calls for it.
• Hands-on LLM application engineering: prompt design, tool/function calling, structured output, context management, and token budgeting.
• Agent orchestration experience: built or operated orchestration with a framework like LangGraph or LangChain, or hand-rolled, beyond single-prompt calls.
• Managed LLM/agent platform in production: AWS Bedrock, Google Vertex AI, or Azure AI Foundry — model invocation, streaming, guardrails, and agent tooling.

Responsabilidades

• Own end-to-end delivery: take a production LLM agent from requirement to production deploy, and defend the architecture and trade-offs directly with the client.
• Build and harden agent orchestration (LangGraph / LangChain or equivalent) — routing, tool-calling, planning, synthesis, and state management.
• Integrate tools over MCP and keep a growing tool surface fast and correct, including BM25, hybrid, or vector retrieval as scale demands.
• Run models on AWS Bedrock and Bedrock AgentCore — model selection/routing, guardrails, memory, and regional residency profiles.
• Build the evaluation harness (golden sets, LLM-as-judge, quality gates wired into CI) and instrument the system with OpenTelemetry for per-session token, cost, and latency attribution.
• Drive down cost and latency with real levers (model routing, prompt caching, payload pruning, parallelizing independent calls) behind a regression gate; build in circuit breakers, fallbacks, and dead-letter handling.
• Design and stage safe write-actions with least-privilege permissions, human-in-the-loop approval, plan versioning, audit trail, and rollback — released behind feature flags to a small cohort first.

Conhecimentos

Python

TypeScript

Go

LLM applications

Prompt design

Tool calling

Orchestration

LangGraph

LangChain

AWS Bedrock

Bedrock AgentCore

Vertex AI

Terraform

CI/CD

IaC

OpenTelemetry

Observability

English

Ferramentas

LangGraph

LangChain

Bedrock

Bedrock AgentCore

Vertex AI

Terraform

Descrição da oferta de emprego

This is not a ticket execution role. You get the problem and the context, you propose the solution, you build it, you ship it, and you defend the technical decisions directly in front of the client. The mandate is to take a production-grade LLM agent from read-only insight toward supervised action — hardening it for scale and staging it up a capability ladder (Explains Recommends Orchestrates Acts). AI-assisted engineering (Claude Code, Cursor, Copilot, or equivalent) is the baseline here, not a differentiator — but you sign the code, and \"the AI wrote it\" is never an answer when something breaks in production.

What you will do

• Own end-to-end delivery: take a production LLM agent from requirement to production deploy, and defend the architecture and trade-offs directly with the client.

• Build and harden agent orchestration (LangGraph / LangChain or equivalent) — routing, tool-calling, planning, synthesis, and state management.

• Integrate tools over MCP and keep a growing tool surface fast and correct, including BM25, hybrid, or vector retrieval as scale demands.

• Run models on AWS Bedrock and Bedrock AgentCore — model selection/routing, guardrails, memory, and regional residency profiles.

• Build the evaluation harness (golden sets, LLM-as-judge, quality gates wired into CI) and instrument the system with OpenTelemetry for per-session token, cost, and latency attribution.

• Drive down cost and latency with real levers (model routing, prompt caching, payload pruning, parallelizing independent calls) behind a regression gate; build in circuit breakers, fallbacks, and dead-letter handling.

• Design and stage safe write-actions with least-privilege permissions, human-in-the-loop approval, plan versioning, audit trail, and rollback — released behind feature flags to a small cohort first.

Required

• 5+ years in software engineering, with at least 2 genuinely at a senior level; strong Python in production, and comfort picking up TypeScript or Go when a project calls for it.

• Hands-on LLM application engineering: prompt design, tool/function calling, structured output, context management, and token budgeting, in a system real users hit.

• Agent orchestration experience: built or operated orchestration with a framework like LangGraph or LangChain, or hand-rolled, beyond single-prompt calls.

• Managed LLM/agent platform in production: AWS Bedrock, Google Vertex AI, or Azure AI Foundry — model invocation, streaming, guardrails, and agent tooling. (We use Bedrock and Bedrock AgentCore; equivalent depth on Vertex AI or Azure transfers directly.)

• Evaluation, retrieval & observability: eval harnesses and golden/reference sets, vector or hybrid search in production, and OpenTelemetry-based distributed tracing with token/cost/latency attribution.

• Production AWS, CI/CD & IaC: real IAM, networking, storage, and observability experience; CI/CD pipelines versioned as code; Terraform in production.

• Working English: comfortable defending system design and technical decisions directly on client calls.