Essential Job Functions: Design, develop, and maintain scalable document ingestion and processing pipelines to support enterprise knowledge management and AI-powered applications. Build and optimize multi-stage data workflows for extracting, transforming, enriching, and indexing content from diverse document formats including PDF, PPTX, DOCX, CSV, and image-based sources. Implement advanced document processing capabilities, including metadata extraction, content chunking, classification, summarization, and information retrieval workflows. Manage vector database indexing strategies and search optimization to ensure high-quality retrieval performance and relevance. Develop and maintain MCP-compatible tool servers that securely expose enterprise databases, APIs, cloud platforms, and external services to AI agents. Design and implement event-driven architectures leveraging messaging and integration services to enable scalable and responsive data processing solutions. Create and deploy data analysis, document intelligence, and knowledge retrieval agents using reusable development templates and platform tooling. Collaborate with cross-functional teams to deliver reliable, scalable, and innovative data engineering and AI-driven solutions.

Desired Candidate Profile

• Bachelor's degree in Computer Science, Data Engineering, Information Technology, Artificial Intelligence, or equivalent combination of education and experience.
• Typically, 3+ years of professional experience in Python development and data pipeline engineering.
• Hands-on experience designing and maintaining ETL/ELT workflows using orchestration platforms such as Dagster, Airflow, or equivalent technologies.
• Strong proficiency with document processing frameworks and libraries for extracting and transforming content from structured and unstructured data sources.
• Experience working with vector databases, embedding models, semantic search technologies, and retrieval optimization techniques.
• Familiarity with Azure data and AI services, including Azure AI Search, Blob Storage, Event Hub, Document Intelligence, or comparable cloud-native platforms.
• Proven experience developing containerized applications and pipeline components using Docker and modern deployment practices.
• Strong understanding of REST API design, integration patterns, and service-oriented architectures.
• Experience building scalable ingestion, indexing, and retrieval systems for data-intensive applications.
• Experience with Retrieval-Augmented Generation (RAG) architectures and enterprise knowledge management platforms is a plus.
• Familiarity with event-driven system design and distributed data processing patterns is preferred.
• Knowledge of AI agent ecosystems, MCP integrations, and modern data-to-agent architectures is highly desirable.
• Strong analytical, troubleshooting, and problem-solving skills.
• Excellent communication and collaboration abilities across technical and business stakeholders.
• Relevant certifications in Azure, Data Engineering, Artificial Intelligence, or Cloud Technologies are a plus.