[Job - 32269] Senior AI Engineer, Brazil

We are looking for a Senior AI Engineer to join a delivery team building Generative AI and Agentic AI solutions.
You will help shape and deliver AI solutions from discovery to production, combining strong software engineering foundations with modern AI capabilities. The role involves hands-on work with RAG, AI agents, enterprise integrations, intelligent workflows, evaluation, observability, and cloud environments.

- Own AI solutions from technical discovery through production and continuous improvement.
- Design and evolve Generative AI, RAG, and agentic applications.
- Define technical approaches considering 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 such as 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 complex production issues involving models, prompts, retrieval, tools, integrations, and infrastructure.
- Collaborate with architects, engineers, product teams, and business stakeholders, providing technical guidance when needed.
- Support other engineers and contribute to engineering standards and best practices for AI solutions.

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

- Knowledge of multi-agent systems and long-running agent workflows.
- Background in Agentic SDLC, coding agents, GitHub, or developer tooling integrations.
- Familiarity with knowledge graphs, GraphRAG, or hybrid retrieval architectures.
- Hands-on knowledge of Datadog LLM Observability, LangSmith, OpenTelemetry, or similar platforms.