[Job-32046] Senior AI Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 2h ago
The date the source published, not the day we noticed it (2026-10-02). Last seen at its source just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Brazil
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $98.1k–143.8k/yr
Middle 50% of 10 listings that do state pay — Engineering · Senior · Brazil · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
lever employer's own board first seen 1h ago · last seen just now
The listing
At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.
With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.
We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.
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.
What you will do:
- 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.
Must-have:
- 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.
- Fluent English for technical discussions with international stakeholders.
Nice-to-have:
- 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.