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 an experienced AI Engineer to join our Data Science & Artificial Intelligence team, designing, architecting, and building advanced AI solutions for our clients and internal teams.
We are looking for someone with strong hands-on experience building and deploying AI solutions in production environments, with the ability to make architectural decisions, define technical standards, and lead complex initiatives throughout the entire solution lifecycle.
As an AI Engineer, you will play a senior technical role, working from architecture and solution design through deployment, operation, and continuous improvement in production.
You will tackle complex challenges across Generative AI, intelligent agents, RAG, enterprise integrations, LLMOps, observability, and reliability, ensuring that AI solutions are scalable, secure, cost-effective, and aligned with business needs.
Design AI solution architectures, considering performance, scalability, security, technical governance, operational costs, and long-term sustainability.
Build AI-powered products and platforms, ensuring strong engineering quality and alignment with business objectives.
Design and develop intelligent agents and multi-agent systems, including planning, reasoning, task orchestration, tool usage, and integration with multiple data sources.
Build advanced Retrieval-Augmented Generation (RAG) architectures, leveraging embeddings, vector databases, GraphRAG, hybrid search, reranking, and grounding techniques.
Integrate AI solutions with enterprise ecosystems, including ERPs, CRMs, transactional systems, data platforms, and business-critical applications.
Evaluate and experiment with LLMs, Small Language Models (SLMs), multimodal models, and other components of the AI technology stack.
Define metrics and AI evaluation strategies to ensure solution quality, accuracy, consistency, and reliability.
Implement observability and monitoring for AI applications, tracking performance, quality, cost, usage, latency, security, and model behavior.
Establish LLMOps practices, including development, testing, versioning, deployment, and monitoring pipelines.
Act as a technical reference, influencing architectural decisions, promoting engineering best practices, and contributing to the technical evolution of the AI practice.
Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Information Systems, or a related field.
Advanced English proficiency, with the ability to communicate effectively in a global environment.
Strong experience developing and deploying Generative AI solutions in production environments.
Experience designing technical architectures for complex, business-critical enterprise solutions.
Experience technically leading complex, multidisciplinary initiatives.
Advanced proficiency in Python.
Experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, or equivalent frameworks.
Experience with OpenAI, Azure OpenAI, Anthropic, Gemini, or similar AI platforms.
Advanced knowledge of RAG architectures, embeddings, and semantic search.
Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or equivalent technologies.
Experience developing APIs and microservices.
Experience with Azure, AWS, or GCP.
Knowledge of observability, monitoring, and reliability engineering practices.
Experience building AI platforms used across multiple clients, products, or business units.
Experience implementing multi-agent architectures in production.
Knowledge of Fine-Tuning, PEFT, LoRA, and other model customization techniques.
Experience with Knowledge Graphs and GraphRAG.
Experience with automated LLM evaluation and AI Evaluation frameworks.
Advanced knowledge of MLOps and LLMOps.
Experience with modern data platforms such as Databricks or Snowflake.
Contributions to technical communities, open-source projects, or AI-related publications.
Advanced certifications in AI, cloud architecture, or software engineering.
This role goes beyond AI experimentation and proof-of-concept development. We are looking for someone who can turn emerging AI capabilities into reliable, scalable, production-ready solutions, making sound technical decisions while keeping business impact at the center of the work.