Senior Data Scientist/AI Data Engineer

Calibre is an employee-owned mission focused solutions and digital transformation company, and they are seeking a highly qualified Hybrid Senior Data Scientist / AI Data Engineer. The role involves supporting the development of AI-enabled applications and reusable data products, combining hands-on AI engineering with data engineering and analytics.

Responsibilities:

• AI Enablement and Retrieval-Based Application Support
• Extend the AI Assistant and LLM to approved data domains and document sets
• Support case/data ingestion, OCR, embeddings, indexing, and vector-store use
• Add Q&A, search, AI-assisted views, and source-cited answers to applications
• Use approved AI services, retrieval guardrails, and query guardrails
• Validate AI features against acceptance criteria, test sets, and quality thresholds
• Data Engineering and Reusable Data Products
• Deliver curated datasets, scheduled extracts, documented pipelines, and data products
• Support SQL, relational databases, data modeling, data quality, and role-based access
• Build applications, dashboards, analytics, AI views, and products with Superset/Python
• Use governed data and customer-provided catalog resources
• Security, Access, Quality, and Delivery
• Apply role-scoped retrieval so users access only authorized data and documents
• Support row-level security and required field-level masking
• Implement or support access-control audit logging
• Support UAT, defect correction, demonstrations, and production adoption
• Prepare templates, onboarding, user/admin docs, knowledge transfer, and transition artifacts
• Support security and ATO inputs, including SSP, assessment, and POA&M items

Requirements:

• Python, SQL, relational databases, analytics, and application/data-product delivery
• Data pipelines, extracts, modeling, quality, curated datasets, and reusable products
• RAG, LLM integration, embeddings, vector stores, ranking, citations, guardrails, and validation
• Document ingestion, text extraction, OCR, indexing, and retrieval-ready content preparation
• RBAC, role-scoped retrieval, row security, masking, audit logging, and secure PII handling
• Apache Superset, Python analytics, and existing Azure or authorized customer environments
• UAT, defect correction, acceptance validation, demos, documentation, templates, and knowledge-transfer materials
• Bachelor's degree in Computer Science or a related field, or equivalent work experience
• 2+ years building applications using retrieval-based AI and LLM services
• 5+ years data engineering with SQL, pipelines, modeling, quality, access, and products
• Experience integrating existing AI, RAG, and LLM capabilities into applications
• Experience with embeddings and vector databases or vector stores
• Experience with document text extraction and OCR
• Experience validating AI features against criteria, test sets, or quality thresholds
• Experience supporting UAT, defects, demos, documentation, and acceptance-ready delivery
• Model Context Protocol (MCP) or similar AI tool-integration standards
• Evaluation/tracing tools such as MLflow
• PostgreSQL read replicas, CDC, dbt semantic modeling, and governed analytics/retrieval
• CI/CD, observability, and Azure-hosted platform environments
• Dashboards, analytics, AI-assisted views, reusable data products, or program enhancements
• Onboarding playbooks, reusable templates, user/admin documentation, and KT materials
• Section 508 accessibility expectations for applications and AI-enabled interfaces
• Privacy, security, records, NIST, FedRAMP, Azure Government, and Federal ATO requirements
• Relevant certifications: Fabric Data Engineer, dbt, Azure AI, Security+, CCSP, CDMP, or CAP