[Job-32046] Senior AI Engineer

- 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.

- Strong Python development skills and 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. - Fluent English for technical discussions with international stakeholders.

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. We are looking for a Senior AI Engineer to join a delivery team building Generative AI and Agentic AI solutions for a global enterprise client. 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: - 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. Must-have: - Strong Python development skills and 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. - Fluent English for technical discussions with international stakeholders. Nice-to-have: - 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. Our benefits: -Health and dental insurance -Meal and food allowance -Childcare assistance -Extended paternity leave -Partnership with gyms and health and wellness professionals via Wellhub (Gympass) TotalPass; -Profit Sharing and Results Participation (PLR); -Life insurance -Continuous learning platform (CI&T University); -Discount club -Free online platform dedicated to physical, mental, and overall well-being -Pregnancy and responsible parenting course -Partnerships with online learning platforms -Language learning platform And many more! More details about our benefits here: At CI&T, inclusion starts at the first contact. If you are a person with a disability, it is important to present your assessment during the selection process. See which data needs to be included in the report by clicking here.This way, we can ensure the support and accommodations that you deserve. If you do not yet have the assessment, don't worry: we can support you in obtaining it. We have a dedicated Health and Well-being team, inclusion specialists, and affinity groups who will be with you at every stage. Count on us to make this journey side by side. Originally posted on Himalayas AI-Engineer, Machine-Learning-Engineer, Senior-AI-Engineer, LLM-Engineer, Senior-AI-ML-Engineer, Senior-AI-Software-Engineer, Senior-AI-Engineering, Senior-Software-AI-Engineer, Senior-Applied-AI-Engineer