Applied AI Engineer - Brazil

Inviso; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

Inviso is partnering with a global client to recruit a Backend / Platform Engineer focused on shipping production-grade AI capabilities. This remote, direct-hire role centers on building and operating enterprise AI features, including agent runtimes, model routing, RAG, and guardrails.

The ideal candidate will move from prototype to monitored production, evaluating AI behavior rigorously and collaborating with product and engineering teams to determine when AI is the right solution.

Qualifications

• Experience shipping software backed by LLMs, ML models, RAG systems, or agentic workflows to real production users.
• Experience designing or implementing agent runtime, orchestration, model routing, tool/function calling, or AI workflow patterns.
• Strong understanding of RAG, grounding, retrieval quality, prompt design, context management, and evaluation approaches.
• Experience building evaluation harnesses, golden sets, regression checks, quality gates, or measurable AI performance frameworks.
• Ability to treat cost, latency, reliability, and safety as first-class engineering constraints.
• Experience building guardrails and controls for AI systems, including untrusted retrieved content, tool output risk, and failure modes.
• Experience monitoring production AI behavior and improving systems based on evidence, not opinion.
• Strong software engineering skills and ability to collaborate with backend, platform, product, and security teams.
• Strong communication and collaboration skills for client-facing consulting environments.
• Pragmatic mindset with the ability to prioritize business value and ship useful AI capabilities quickly and responsibly.

Responsibilities

• Build and operate AI capabilities across an enterprise platform, including agent runtimes, model routing, RAG, tool/function calling, and guardrails.
• Support evaluation, monitoring, cost management, latency optimization, and production quality controls.
• Collaborate with backend, platform, product, and security teams to ship reliable AI features.
• Engage with client teams to influence AI product decisions and demonstrate value.

Skills

LLMs production

Agent runtime design

Model routing

RAG systems

AI evaluation

Guardrails

Production monitoring

Backend collaboration

Client-facing

Cost & latency awareness

Tools

Claude

GPT

Azure OpenAI

Open-source models

Job description

Inviso is partnering with one of our clients to find an experienced Backend / Platform Engineer to join their team working remotely. Inviso is managing the recruitment and placement process for this opportunity. This is a remote, direct-hire permanent employee position for our client that is a global multi-billion dollar software conglomerate.

The Applied AI Engineer is responsible for building and operating AI capabilities across an enterprise platform, including agent runtimes, model routing, RAG, tool/function calling, guardrails, evaluation, monitoring, cost management, latency optimization, and production quality controls.

This role is focused on shipping AI that works in production, not demos. The ideal candidate understands how to move from prototype to monitored production systems, can evaluate AI behavior rigorously, and can partner with product and engineering teams to decide when AI is the right solution and when it is not.

Experience & Skills Required

• Experience shipping software backed by LLMs, ML models, RAG systems, or agentic workflows to real production users.
• Experience designing or implementing agent runtime, orchestration, model routing, tool/function calling, or AI workflow patterns.
• Strong understanding of RAG, grounding, retrieval quality, prompt design, context management, and evaluation approaches.
• Experience building evaluation harnesses, golden sets, regression checks, quality gates, or measurable AI performance frameworks.
• Ability to treat cost, latency, reliability, and safety as first-class engineering constraints.
• Experience building guardrails and controls for AI systems, including untrusted retrieved content, tool output risk, and failure modes.
• Experience monitoring production AI behavior and improving systems based on evidence, not opinion.
• Strong software engineering skills and ability to collaborate with backend, platform, product, and security teams.
• Strong communication and collaboration skills for client-facing consulting environments.
• Pragmatic mindset with the ability to prioritize business value and ship useful AI capabilities quickly and responsibly.

Desired Qualifications

• Experience with Claude, GPT, Azure OpenAI, open-source models, model routing, fine-tuning, distillation, or small/edge models.
• Experience with MCP, A2A, multi-agent orchestration, AI tool calling, or agent evaluation.
• Experience with LLM-as-judge approaches calibrated against human evaluation.
• Experience with semantic knowledge management, ontologies, knowledge graphs, or semantic layers.
• Experience with AI development lifecycle practices across data, build, evaluation, deployment, monitoring, and continuous improvement.
• Experience using AI-assisted development tools while maintaining strong review, testing, and safety discipline.

Success Criteria

Success in this role is measured by the ability to deliver AI capabilities that are useful, reliable, measurable, safe, and cost aware. Applied AI Engineers are expected to turn experiments into production features, build evaluation discipline into the delivery process, and help client teams make informed decisions about where AI creates real value.