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).