AI Engineer
Papigen Private Limited · Not specified · onsite · Posted 5d ago
Not disclosed1–3 yrsfulltimeSourced from
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About this role
Role Overview
The AI Engineer will develop document-AI pipelines, LLM prompts, Retrieval-Augmented Generation (RAG) solutions, and evaluation datasets for enterprise applications. The role includes processing scanned and digital documents, extracting and classifying information, improving retrieval quality, and integrating AI services with OutSystems through APIs.
Candidates must have at least 1 year of hands-on LLM/RAG development experience and demonstrate working solutions with measurable outcomes.
Key Responsibilities
The AI Engineer will develop document-AI pipelines, LLM prompts, Retrieval-Augmented Generation (RAG) solutions, and evaluation datasets for enterprise applications. The role includes processing scanned and digital documents, extracting and classifying information, improving retrieval quality, and integrating AI services with OutSystems through APIs.
Candidates must have at least 1 year of hands-on LLM/RAG development experience and demonstrate working solutions with measurable outcomes.
Key Responsibilities
- Build document-processing pipelines for extraction, classification, and information retrieval.
- Develop RAG pipelines using embeddings, vector databases, and LLM APIs.
- Create, test, version, and maintain prompts and prompt templates.
- Build evaluation datasets to measure answer accuracy and consistency with source documents.
- Process scanned PDFs using OCR and other document-processing services.
- Tune retrieval strategies, chunking, embeddings, and ranking to improve answer relevance.
- Expose AI capabilities through REST APIs for integration with OutSystems applications.
- Write automated tests and evaluate AI-generated responses against source documents.
- Monitor pipeline performance and improve the reliability of AI outputs
- 3+ years of applied AI experience.
- At least 1 year of hands-on LLM/RAG development experience.
- Practical experience building RAG pipelines or AI agents with delivered results.
- Python programming and experience integrating LLM APIs.
- Understanding of embeddings, vector databases, and document retrieval.
- Experience building document-AI or GenAI solutions.
- Knowledge of OCR and processing scanned PDFs or other document formats.
- REST API development and integration experience.
- Ability to create evaluation datasets, compare AI responses against source documents, and improve retrieval quality.
- Experience writing tests and validating AI outputs rather than relying solely on manual review.
- OpenAI API or Azure OpenAI.
- LangChain or LlamaIndex.
- Vector databases and vector search technologies.
- OCR and document-processing services.
- OutSystems API integration.
- Experience in enterprise document intelligence, procurement, or public-sector systems.
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Skills
document processing servicesRest Api DevelopmentRetrieval Augmented Generationpython programmingAzure OpenAIvector databasesEmbeddingsLarge Language Models (LLMs)OpenAI SDKOCRLangchainLlamaindexdocument retrieval