Software Engineer
Software Engineer building AI-powered infrastructure, data pipelines, and agentic workflows.
From production RAG retrieval engines to MCP-based agent tooling and multi-model LLM orchestration, I build highly concurrent systems that solve complex, large-scale data problems.
About
I am a Software Engineer focused on backend architecture, AI systems, and cloud automation. My work centers on building scalable, fault-tolerant Python pipelines, vector retrieval systems, and API integrations that transform high-friction operational workflows into deterministic, repeatable software.
Featured Projects
View all 19 →AI-Driven Issue Tracking & Analytics Pipeline
An 8-part AI pipeline that maps FedRAMP 20x controls to a client's technology stack, identifies compliance gaps against live Vanta test data, generates remediation plans, and uploads a fully structured Epic → Task → Sub-task hierarchy to Jira.
- →Processed 3 control families (KSI: 56 controls, ADS: 20 controls, CCM: 3 controls) through the full pipeline end-to-end
- →Generated 582 Jira tickets (Epics + Tasks + Sub-tasks) across all families with proper hierarchy and audit-ready descriptions
- →Reduced the control-to-ticket lifecycle from weeks of manual analysis to a single pipeline run per family
- →Every AI call logged with prompt sent and response received — full audit trail for compliance review
System Architecture & Design
A high-level architecture diagram of the compliance automation platform, illustrating the orchestration layer, dependency injection, strategy-based backend execution, logic pipelines, adapter protocols, and external provider integrations.
- →Established clear separation between orchestration, logic, and infrastructure layers
- →Enabled 15+ compliance workflows to share pipelines, services, and configuration
- →Decoupled external provider integrations through adapter protocols and lazy initialization
Cloud Data Normalization Pipeline
A data pipeline that transforms raw cloud inventory exports and Tenable scan data into a FedRAMP Appendix M submission-ready workbook.
- →Eliminated manual reconciliation across many asset categories
- →Produced submission-ready Appendix M workbooks directly from exported data
- →Created a repeatable workflow for a tedious compliance reporting task
Jira MCP Server for AI Agent Workflows
An MCP server that exposes Jira operations as AI-callable tools, allowing LLM clients to search, create, update, transition, and comment on issues.
- →Enabled AI agents to interact directly with Jira instead of relying on copy-paste handoffs
- →Connected analysis workflows to actionable remediation tracking
- →Demonstrated practical use of emerging MCP-based agent tooling
Production RAG Infrastructure on AWS
A reusable retrieval layer built on AWS OpenSearch and Amazon Bedrock embeddings that grounds multiple LLM pipelines in system-specific documentation.
- →Enabled grounded retrieval across multiple AI workflows
- →Centralized retrieval infrastructure for compliance and documentation pipelines
- →Improved reliability of generated outputs by attaching them to semantic search results
Prompt Engineering for Anti-Hallucination Evidence Generation
A multi-layer prompt and validation architecture that prevents LLM hallucinations in compliance evidence generation through structured inputs, hard constraint gates, and a 4-phase validation pipeline.
- →Eliminated AWS service name leakage into abstract classification outputs via hard-coded regex blocklist
- →Reduced misclassification of process-only controls through deterministic escape hatches that bypass the LLM entirely
- →Established a gold-set validation framework with 10 analyst-authored test cases and 8 codified divergence categories
More Projects
View all 19 →Vanta Compliance Gap Analyzer
Integrated with Vanta's GraphQL API to fetch, paginate, categorize, and structure compliance test failures for remediation planning.
AI-Driven NIST 800-53 Component Mapping Engine
Mapped control parts to implementing cloud services using a multi-stage LLM workflow with extraction and triage passes.
AI-Powered AWS Audit Evidence Command Generator
Generated and validated read-only AWS CLI commands for gathering evidence against NIST 800-53 control parts.
Google Docs Feedback Loop System
Closed-loop refinement system that polled Google Doc comments via the Docs API, classified comment relevance with GPT, generated refined replacements, and validated rewrites through a verification pass before applying batchUpdate edits — turning client review into automated revisions.
FedRAMP Privacy Plan Generator
Generated FedRAMP Privacy Plan deliverables across NIST 800-53 Rev5 Privacy baseline by flattening nested compliance domains into tabular DataFrames with index-based JSON enrichment (O(1) lookups by control ID) for moderate-baseline cross-references.
Supply Chain Risk Management (SCRM) Plan Generator
Two-phase SCRM Plan generator using a dedicated OpenAI Assistant per domain, with hard-constraint prompts ("Do NOT add, delete, reorder…") and vector-store-scoped retrieval grounding for FedRAMP Rev5 supply-chain controls.
Skills
Core
Tools
Contact
I’m interested in building AI-powered systems that solve real operational problems.