Senior AI Engineer

CI&TGenerative AI

Listing location: Brazil; Workplace type: Remote; Structured job location: BrazilFull-TimeSenior

Salary not disclosed

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Job Details

Languages: English for technical discussions with international stakeholders.
Required Skills: DockerCI/CDGenerative AILangChain

Have solid software engineering foundations and a proven ability to design and deliver production-grade Generative AI and LLM-based solutions.
Apply software design principles including modularity, separation of concerns, testability, maintainability, resiliency, and clean interfaces.
Design services, APIs, asynchronous workflows, and distributed components for production environments.
Have hands-on knowledge of RAG and agentic AI patterns, including tool use, function calling, orchestration, Human-in-the-Loop, and guardrails.
Understand embeddings, vector search, retrieval strategies, chunking, reranking, and enterprise knowledge retrieval.
Be proficient with AI frameworks or SDKs such as LangGraph, LangChain, Semantic Kernel, LlamaIndex, OpenAI Agents SDK, or Strands.
Have practical knowledge of multiple LLM providers, such as OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, or AWS Bedrock.
Assess trade-offs between models, providers, orchestration approaches, retrieval strategies, and infrastructure options.
Understand AI evaluation and observability, including quality, tracing, latency, token usage, failures, and cost.
Understand cloud-native practices including scalability, resiliency, secrets and configuration management, observability, and access control.
Have strong working knowledge of Docker, CI/CD, automated testing, and version control.
Use English for technical discussions with international stakeholders.

Own AI solutions from technical discovery through production and continuous improvement.
Design and evolve Generative AI, RAG, and agentic applications.
Define technical approaches that account for quality, scalability, security, cost, maintainability, and operational impact.
Design agents and workflows that interact with tools, APIs, databases, messaging systems, and enterprise platforms.
Define orchestration patterns including Human-in-the-Loop, long-running workflows, retries, fallback strategies, and deterministic controls.
Establish evaluation, observability, guardrails, and reliability practices for AI applications.
Guide model, retrieval, framework, and infrastructure choices based on technical trade-offs.
Troubleshoot production issues involving models, prompts, retrieval, tools, integrations, and infrastructure.
Collaborate with architects, engineers, product teams, and business stakeholders; support other engineers and contribute to engineering standards.

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