Senior Software Engineer

Role overview Design and deliver scalable cloud services that use large language models and document-focused AI. The work centers on Python backend development in Microsoft Azure, including retrieval and search workflows, model integrations, and document ingestion. You’ll collaborate with other functions to define architecture and implementation plans, and help maintain reliable services from development through deployment.

Responsibilities - Build and maintain backend services and APIs with Python and FastAPI, and deploy cloud-native applications using Azure services. - Integrate language models and AI capabilities, including GPT-family models and related assistant tools. - Implement retrieval-augmented generation workflows, covering embeddings, vector search, indexing, semantic retrieval, and reranking. - Improve prompts and orchestration flows to support answer quality and user experience. - Package and deploy services with Docker; maintain Git-based workflows and CI/CD processes in Linux environments. - Develop NoSQL storage and OCR-based document ingestion, and contribute unit tests and quality assurance.

Requirements - Degree in computer science, software engineering, or a related field, or equivalent practical experience. - Strong Python and object-oriented development skills. - Hands-on experience with Azure and cloud-native application development. - Experience with Docker, container-based deployments, and production APIs or services. - Practical experience integrating large language models and understanding RAG, vector databases, embeddings, semantic search, indexing, prompt design, and reranking. - Familiarity with Git collaboration, Linux troubleshooting, NoSQL databases, OCR workflows, and automated unit testing. - Strong problem-solving and communication skills.

Nice to have - React and TypeScript experience for full-stack development. - Experience with document intelligence, including parsing and transforming DOCX, PPTX, or PDF files and extracting, classifying, or enriching their contents. - Familiarity with document processing pipelines, metadata enrichment, or vector search services such as Azure AI Search. - Experience deploying AI workloads in production.