About this role
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 AI Engineer to join a delivery team building Generative AI solutions for a global enterprise client. You will take LLM-based use cases from PoC to production, working on RAG systems, document intelligence, and conversational AI, mainly on Azure with some AWS workloads.
Must-have
4+ years in Machine Learning / AI engineering, with at least 2 years hands-on with LLMs and Generative AI.
Proven experience delivering RAG or LLM-based applications beyond PoC stage.
Strong Python skills and experience with LangChain (or similar frameworks like LlamaIndex or Semantic Kernel).
Hands-on experience with Azure AI services: Azure OpenAI, Azure AI Search, Azure AI Foundry/AI Studio, Cosmos DB, and Blob Storage.
Experience with vector databases/indexes (Azure AI Search, FAISS, or similar) and embedding models.
Experience with multiple LLM families (OpenAI GPT, Anthropic Claude, Google Gemini, open-source models like Llama via Hugging Face).
CI/CD experience (Azure DevOps and/or GitHub Actions) and familiarity with MLOps tooling such as MLflow.
Fluent English for daily communication with international stakeholders.
Nice-to-have
AWS ML services: SageMaker, Textract, Comprehend, Bedrock.
Document AI and OCR: LayoutLM, Layout-Parser, Tesseract/EasyOCR, Azure Document Intelligence.
Computer Vision (object detection, e.g. Detectron2) and multimodal/vision LLMs.
Speech recognition (ASR) solutions.
Fine-tuning experience (LoRA/PEFT) on open-source models.
LLM caching and performance (Redis, semantic cache).
Kubeflow, Kubernetes, or containerized model serving.
Classical ML: anomaly detection, recommendation engines, NLP with spaCy.
Experience with agentic patterns (tool use, multi-agent orchestration, MCP).
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