Senior AI / Backend Engineer – RAG & Intelligent Document Processing

• Design and develop production-grade AI/ML and backend systems using Python and FastAPI/Django. 

• Build OCR and Intelligent Document Processing pipelines for extracting structured information from documents. 

• Design document ingestion, preprocessing, parsing, chunking, embedding, retrieval, and validation workflows. 

• Build and optimize RAG pipelines using vector databases and semantic search. 

• Integrate and work with LLMs such as OpenAI, Claude, Llama and other foundation models. 

• Develop and evaluate prompting, structured outputs, tool calling and agentic workflows. 

• Design AI systems capable of handling complex and variable document formats. 

• Implement document-level and field-level validation to improve extraction accuracy. 

• Develop REST APIs and microservices for AI and document-processing services. 

• Work with PostgreSQL, Redis and vector databases such as pgvector, ChromaDB or similar technologies. 

• Develop asynchronous processing pipelines using tools such as Celery, queues or event-driven architectures. 

• Implement AI evaluation frameworks covering accuracy, hallucination, retrieval quality and extraction reliability. 

• Explore and implement AI agents and MCP-based integrations where appropriate. 

• Deploy AI services using Docker and cloud platforms such as AWS or Azure. 

• Collaborate with software engineers to integrate AI capabilities into production applications. 

• Ensure appropriate security and privacy controls when handling sensitive documents and data. 

• Monitor production AI systems and continuously improve performance, accuracy and reliability. 

Within the first 6 months, you will have helped establish reliable production AI infrastructure capable of: 

Document → OCR → Extraction → Validation → Embeddings → Retrieval → RAG/LLM → Structured Output → Business Application 

The focus is not simply on "using AI", but on building accurate, scalable and production-ready AI systems. 

• 5+ years of professional software/backend development experience. 

• Strong Python development experience. 

• Strong experience with FastAPI, Django or similar backend frameworks. 

• Hands-on experience building RAG applications. 

• Practical experience with LLM APIs and GenAI applications. 

• Understanding of embeddings, semantic search and vector databases. 

• Experience with OCR, document processing or IDP is highly preferred. 

• Strong REST API and microservices experience. 

• PostgreSQL and Redis experience. 

• Experience with asynchronous processing/background jobs. 

• Docker and cloud deployment experience. 

• Strong understanding of API security, authentication and authorization. 

• Git and CI/CD experience. 

• Experience with OpenAI, Claude, Llama or similar LLMs. 

• LangChain, LlamaIndex or equivalent frameworks. 

• pgvector, ChromaDB, Pinecone, Weaviate or similar. 

• MCP / agentic AI systems. 

• Document AI, Google Cloud Vision, AWS Textract or Azure Document Intelligence. 

• LLM fine-tuning, quantization or inference optimization. 

• Experience with Kubernetes. 

• Experience with regulated data, healthcare, finance or other sensitive-data environments. 

• Experience designing AI evaluation and monitoring systems.