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

OpsBrasil Serviços Cloud LTDA; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

OpsBrasil Serviços Cloud LTDA seeks an experienced software engineer to drive production-grade LLM agent projects. You will own design, tooling, and deployment from requirements through client discussions, focusing on orchestration, tool calls, and secure write actions.

You’ll operate across AWS Bedrock, LangGraph, and Terraform with strong emphasis on cost, latency, and observability. The role requires hands-on Python, LLM design skills, and the ability to defend technical trade-offs during

Qualifications

• 5+ years in software engineering, with at least 2 at a senior level.
• Hands-on LLM application engineering: prompt design, tool use, structured output.
• Experience with agent orchestration beyond single-prompt calls.
• Production IAM, networking, storage, and observability in cloud environments.
• Ability to defend design decisions directly on client calls.

Responsibilities

• Own end-to-end delivery: from requirements to production deployment and client defense.
• Build and harden agent orchestration and routing, tool-calling, planning and state management.
• Integrate tools (BM25, vector/hybrid search) for scalable retrieval.
• Run models on AWS Bedrock and Bedrock AgentCore with guardrails and memory management.
• Create evaluation harnesses and instrument with OpenTelemetry for per-session metrics.
• Optimize cost and latency with routing, caching, pruning, and circuit breakers.
• Design safe write-actions with least-privilege permissions and feature-flag releases.

Skills

Python production-grade

LLM application engineering

Prompt design

Orchestration & tooling

System design & architecture

Tools

AWS Bedrock

LangChain

LangGraph

Bedrock AgentCore

Terraform

OpenTelemetry

CI/CD (GitHub Actions)

Job description

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

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.

Requirements 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.

Highlights

Tech stack AWS Bedrock, Bedrock AgentCore, LangGraph/LangChain, MCP, Terraform, GitHub Actions/GitLab CI, OpenTelemetry Originally posted on Himalayas