Audit & Traceability Engineer – AI/Agentic Automation
Experience: 6–9 years
Domain: Healthcare / Medicaid
Project: R2R Agentic Automation
Rate -$80/hr
Remote / WFO / Hybrid: Remote
Hire Type (FTE/Contract): Contract
Project Duration: 5 months – most likely to extend till 12 months
Expected Start Date: 11/02/2026
Role Summary
We are looking for a hands-on Audit & Traceability Engineer to design and implement end-to-end auditability, data lineage, observability, and evidence management for AI/agentic workflows supporting Medicaid Record-to-Report (R2R) processes. The role will ensure that data, calculations, agent decisions, tool/API calls, exceptions, and human approvals are traceable, reproducible, explainable, and audit-ready.
Key Responsibilities
- Implement end-to-end data lineage and decision provenance from source systems through agent workflows and human approvals.
- Build structured audit logging, telemetry, tracing, and monitoring for AI agents and APIs.
- Capture agent execution details including prompt/model versions, tool calls, inputs/outputs, errors, retries, and workflow states.
- Enable historical replay and reproducibility through versioned data, rules, prompts, models, and configurations.
- Implement traceability for exceptions, reconciliations, approvals, overrides, and control evidence.
- Develop automated tests for lineage completeness, reconciliation, regression, security controls, and failure recovery.
- Ensure audit traces comply with HIPAA, PHI/PII protection, RBAC, least privilege, and data-retention requirements.
- Collaborate with AI, data, Medicaid, finance, security, QA, and Controller teams.
Required Technical Skills
- Strong Python and SQL
- Data lineage, metadata, and provenance
- Audit logging and distributed tracing
- AI/LLM and agentic workflow traceability
- REST APIs and integration
- Workflow orchestration and state management
- Observability, telemetry, and monitoring
- Automated testing and reconciliation
- IAM/RBAC, encryption, secrets management, and secure logging
- Understanding of LLMs, prompts, tool/function calling, human-in-the-loop, and AI guardrails
Preferred Technology Experience
- Azure
- Databricks / Delta Lake
- Unity Catalog
- MLflow tracing and evaluation
- Azure OpenAI
- LangChain / LangGraph or equivalent agent frameworks
- MCP or similar agent-to-tool integration patterns
Healthcare / Medicaid Knowledge
Working knowledge of:
- Medicaid enrollment and eligibility
- Capitation and reconciliation
- Claims and encounters
- 834, 820, 837, and 835 healthcare transactions
- HIPAA and PHI/PII controls
- Healthcare financial/R2R processes
Ideal Candidate
A strong software/data/AI engineer with auditability and observability experience, capable of making every material AI-assisted R2R outcome traceable from source data through calculation, agent execution, exception handling, and human approval.