Senior Ai & Document Intelligence Lead - Responsible Ai

Paralucent is seeking a Senior AI & Document Intelligence Lead to design and validate AI-assisted capabilities for high-stakes, regulated workflows. You will lead OCR, classification, metadata extraction, summarization and recommendation use cases while ensuring source grounding, confidence controls, human validation, privacy, security, traceability and reliable fallback.
This is not an experimentation-only role;
it requires disciplinedmovement from evaluation to controlled production use.
What You'll Do
Define the AI/document-intelligence architecture, providers, models, orchestration, data boundaries and deployment options.
Design OCR, document classification, metadata extraction, language detection, completeness checks, summarization and recommendation capabilities.
Create representative evaluation datasets covering languages, document types, scan quality, ambiguity and edge cases.
Define measures such as precision, recall, classification accuracy, confidence calibration, false negatives, unsupported outputs and human acceptance.
Implement source grounding, model/version logging, prompt/template governance, review/override flows and fallback to human processing.
Partner with business and legal/control stakeholders to define permitted and prohibited AI actions.
Ensure sensitive information, prompts, embeddings, retrieval content and logs are handled according to approved privacy/security requirements.
Support Responsible AI reviews, model changes, monitoring, drift/error analysis and incident response.
Clearly distinguish production‑proven, configurable, developmental and experimental capability.
Coach engineering and QA teams on safe implementation and AI‑specific testing.
What Success Looks Like
AI use cases are selected based on measurable operational value and risk, not novelty.
Evaluation evidence supports every production recommendation and known limitations are visible.
Low‑confidence, ambiguous and sensitive outcomes reliably route to qualified human validation.
AI outputs remain traceable to source content and human decisions.
Models can be changed, monitored or disabled without disrupting the controlled workflow.
8+ years across machine learning, applied AI, data science or intelligent document processing, with senior technical ownership.
Demonstrated production experience with OCR, document intelligence, NLP/LLMs or retrieval‑grounded enterprise applications.
Experience in banking/financial services preferred;
regulated‑industry AI experience accepted.
Strong knowledge of evaluation design, confidence calibration, error analysis, human‑in‑the‑loop controls and AI observability.
Understanding of privacy, security, model risk, data residency, retention and audit requirements.
Ability to explain AI trade‑offs and limitations clearly to executives, control functions and engineering teams.
Preferred Experience
Experience with Azure AI/OpenAI, AWS AI services or comparable enterprise platforms.
Experience with English/French document processing.
Experience with legal, compliance, investigations, disclosure or document‑heavy operational workflows.
Responsible AI, cloud AI or machine‑learning certification.
Role Scorecard: Illustrative MeasuresFinal targets and cadence will be confirmed for the specific assignment. Typical measures include:
Evaluation coverage and quality
Production acceptance of AI use cases
False-negative/unsupported-output trends
Human acceptance and override patterns
Confidence calibration and fallback performance
Model monitoring and issue closure
Responsible AI evidence readiness
How You Demonstrate Our Core ValuesDriven:
Moves important work forward, recovers quickly from blockers and focuses on meaningful outcomes.
Takes Ownership:
Closes loops, communicates risks early and remains accountable across handoffs.
Creative Problem Solver:
Clarifies the real problem and proposes practical options with trade‑offs.
Humbly Confident:
Brings sound judgment, explains reasoning and actively invites challenge and feedback.
Customer Centric:
Understands operational reality, success measures and the impact of decisions on users and the institution.
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