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Senior
Registration: 08.04.2026
Chris Love
Specialization: AI Developer / Systems Architect
— Senior AI Systems Engineer with 30+ years of software engineering experience spanning web platforms, distributed systems, and enterprise-grade backend architecture.
— Over the past several years, proficient in designing and shipping production LLM-integrated systems including AI workflow automation engines, retrieval-augmented generation (RAG) pipelines, structured prompt architectures, and multi-agent orchestration frameworks.
— Experienced in building reliable, cost-aware, and auditable AI applications that integrate APIs, vector search, structured memory models, and event-driven workflows.
— Proven track record of delivering scalable SaaS platforms, multi-role systems, compliance-sensitive data workflows, and automation engines across healthcare, legal, field services, and analytics domains.
— Former 14-year Microsoft MVP with deep background in web architecture and systems design, now specializing in practical AI integration — bridging large language models with real-world business workflows to build intelligent, production-ready systems that reduce operational friction and increase automation reliability.
— Designs AI systems that are deterministic, auditable, cost-aware, and production-stable — not demo prototypes.
AI Systems & LLM Architecture:
— OpenAI API, structured outputs, function/tool calling.
— Retrieval-Augmented Generation (RAG) pipelines.
— Multi-agent orchestration workflows.
— Prompt architecture & evaluation loops.
— Schema validation & hallucination mitigation.
— Context window management & memory modeling.
— Vector databases & embedding strategies.
Backend & Systems Architecture:
— Node.js (Express), Python (FastAPI).
— PostgreSQL, MongoDB.
— RESTful API design & webhook orchestration.
— Event-driven systems & background workers.
— Multi-tenant SaaS architecture.
— SLA engines & escalation workflows.
Workflow Automation & Integrations:
— API-heavy platform integrations.
— CRM-style contact intelligence systems.
— Document generation & templating systems.
— Authentication & role-based access control.
— Third-party services (Stripe, Twilio, SendGrid, etc.).
Infrastructure & Deployment:
— AWS (Lambda, CloudFront, Route 53, ACM, Lightsail, S3).
— Observability & logging strategies.
— Cost-aware LLM deployment design.
Foundations:
— Distributed systems thinking.
— Secure data handling (healthcare, legal domains).
— Senior AI Systems Engineer with 30+ years of software engineering experience spanning web platforms, distributed systems, and enterprise-grade backend architecture.
— Over the past several years, proficient in designing and shipping production LLM-integrated systems including AI workflow automation engines, retrieval-augmented generation (RAG) pipelines, structured prompt architectures, and multi-agent orchestration frameworks.
— Experienced in building reliable, cost-aware, and auditable AI applications that integrate APIs, vector search, structured memory models, and event-driven workflows.
— Proven track record of delivering scalable SaaS platforms, multi-role systems, compliance-sensitive data workflows, and automation engines across healthcare, legal, field services, and analytics domains.
— Former 14-year Microsoft MVP with deep background in web architecture and systems design, now specializing in practical AI integration — bridging large language models with real-world business workflows to build intelligent, production-ready systems that reduce operational friction and increase automation reliability.
— Designs AI systems that are deterministic, auditable, cost-aware, and production-stable — not demo prototypes.
AI Systems & LLM Architecture:
— OpenAI API, structured outputs, function/tool calling.
— Retrieval-Augmented Generation (RAG) pipelines.
— Multi-agent orchestration workflows.
— Prompt architecture & evaluation loops.
— Schema validation & hallucination mitigation.
— Context window management & memory modeling.
— Vector databases & embedding strategies.
Backend & Systems Architecture:
— Node.js (Express), Python (FastAPI).
— PostgreSQL, MongoDB.
— RESTful API design & webhook orchestration.
— Event-driven systems & background workers.
— Multi-tenant SaaS architecture.
— SLA engines & escalation workflows.
Workflow Automation & Integrations:
— API-heavy platform integrations.
— CRM-style contact intelligence systems.
— Document generation & templating systems.
— Authentication & role-based access control.
— Third-party services (Stripe, Twilio, SendGrid, etc.).
Infrastructure & Deployment:
— AWS (Lambda, CloudFront, Route 53, ACM, Lightsail, S3).
— Observability & logging strategies.
— Cost-aware LLM deployment design.
Foundations:
— Distributed systems thinking.
— Secure data handling (healthcare, legal domains).
Skills
JavaScript
Open AI Integrations
Progressive Web Apps
Artificial Intelligence
LLM
Nodejs
RAG systems
Secure AI Platforms
Work experience
Senior Architect - Developer
06.2019 - 06.2024 |Home Health Care Appointment Scheduling and Audit Tracking
MariaDB, Nodejs, Vanilla JS/MVC Progressive Web App
● I was the architect, developer and technical lead.
● My responsibilities included developing an offline-first client application for home health care professionals, admin and SaaS management portals.
Lead Systems Architect / AI Systems Engineer
SharpAI - LLM-Powered Knowledge Intelligence Platform
AI, LLM, JSON, OpenAI, RAG
Delivered a production-capable AI knowledge layer ready for member-facing deployment, including guardrails, structured output enforcement, and cost-aware orchestration.
Key Contributions:
● Architected a retrieval-enhanced LLM system integrating 40,000+ embedded domain articles using Chroma vector storage to ground model responses in proprietary content.
● Designed controlled prompt architecture to enforce domain alignment, response structure, and deterministic formatting.
● Implemented structured JSON output validation with schema enforcement and retry logic to prevent malformed or hallucinated fields.
● Built API-driven integration layer with OpenAI models, including context injection, response parsing, and failure handling.
● Engineered semantic search and relevance filtering pipelines to dynamically inject authoritative context into responses.
● Designed guardrails to reduce hallucination and improve trustworthiness in member-facing outputs.
● Implemented logging and observability patterns for monitoring token usage, failure cases, and output consistency.
● Collaborated with product leadership to align AI behavior with UX expectations and platform constraints.
Architecture Highlights:
● Retrieval-Augmented Generation (RAG-style architecture).
● Vector embeddings + similarity search.
● Structured output enforcement.
● Context window optimization.
● API orchestration layer.
● Production web integration.
Lead Systems Architect / AI Systems Engineer
IntentLens - AI-Powered Decision & Recommendation Engine
AI, LLM, OpenAI API, JSON
Designed and implemented an AI-driven recommendation and structured analysis engine that transforms user intent and contextual inputs into personalized product insights and decision support outputs.
Key Contributions:
● Architected an LLM-integrated recommendation engine leveraging OpenAI APIs, paired with Amazon’s Product API to generate context-aware product intelligence and personalized guidance.
● Designed structured prompt workflows to interpret user intent signals and transform them into deterministic recommendation logic.
● Implemented controlled output formatting with schema-based validation to ensure consistent, machine-readable results.
● Integrated external data sources (e.g., product metadata, affiliate APIs) into the LLM reasoning loop to enhance contextual accuracy.
● Engineered a hybrid reasoning pipeline combining deterministic filtering logic with LLM-based qualitative analysis.
● Built structured output pipelines enabling downstream automation, reporting, and dynamic UI rendering.
● Designed guardrails to reduce hallucination risk and ensure recommendations aligned with verified product attributes.
● Optimized token usage and response latency for scalable, cost-aware deployment.
Architecture Highlights:
● LLM API integration (OpenAI).
● Structured output enforcement (JSON schema validation).
● Hybrid deterministic filtering + LLM-based reasoning pipeline.
● Context-aware prompt engineering.
● External API integration & data normalization.
● Automated recommendation workflow orchestration.
LLM-Powered Structured Extraction & Career Intelligence System
Resume Parsing & Skills Gap Analysis Engine
OpenAI API (GPT-4 structured outputs), Node.js, JSON Schema validation, PostgreSQL, Semantic scoring logic, Rule-based validation engine
Designed and implemented an AI-driven resume analysis engine that converts unstructured resumes into structured, machine-readable profiles while generating personalized career insights and skills gap recommendations.
Key Contributions:
Engineered a multi-stage LLM pipeline to:
● Parse resumes (PDF/DOCX/text) into structured JSON schemas.
● Extract skills, roles, seniority, industries, certifications, and tenure.
● Normalize inconsistent formatting and ambiguous job titles.
● Implemented schema-validated structured outputs with retry logic to eliminate malformed responses and hallucinated fields.
Designed skills taxonomy alignment layer to:
● Map extracted skills to standardized skill clusters.
● Identify missing competencies relative to target roles.
● Generate prioritized upskilling recommendations.
Built deterministic validation rules to:
● Detect timeline inconsistencies.
● Identify inflated or duplicated experience entries.
● Flag incomplete skill declarations.
● Integrated LLM reasoning with rule-based scoring to produce:
● Role fit scoring.
● Skill gap analysis.
● Personalized resume improvement suggestions.
Architected system for extensibility toward:
● Job description comparison (JD vs resume matching).
● Interview preparation insights.
● Career trajectory modeling.
Lead Systems Architect / Full-Stack Engineer
Clinical Trial Patient Engagement Platform
Node.js, MongoDB, RESTful API
Architected and delivered a multi-role clinical trial engagement platform designed to streamline patient onboarding, communication, visit tracking, and regulatory documentation workflows for research organizations.
Key Contributions:
● Designed a secure, role-based web platform supporting patients, coordinators, investigators, and administrators.
● Architected relational data models for patient enrollment, visit schedules, consent documentation, and trial milestones.
● Implemented automated workflow routing for approvals, reminders, and compliance-driven task sequencing.
● Built structured reporting dashboards to monitor trial engagement, retention metrics, and protocol adherence.
● Integrated secure messaging and notification systems to improve participant communication and reduce manual follow-up.
● Engineered audit logging and activity tracking to support regulatory oversight and data traceability.
● Deployed secure infrastructure aligned with healthcare data handling standards.
Architecture & Stack:
● Node.js backend services.
● MongoDB relational schema design.
● RESTful APIs.
● Role-based access control (RBAC).
● Workflow automation engine.
● Secure cloud deployment with audit logging.
Senior Systems Architect / Lead Engineer
Enterprise Fleet & Bulk Order Management Platform
API, CRM
● Architected and led redevelopment of a mission-critical fleet and bulk order management platform for a major U.S. automotive distributor serving commercial and government buyers across multiple states.
● The system managed high-volume fleet orders, vehicle customization workflows, dealer coordination, and customer lifecycle tracking — replacing fragmented legacy systems with a centralized, scalable platform.
Key Contributions:
● Designed and implemented multi-role enterprise workflow system (sales reps, regional managers, dealers, operations, finance).
● Modeled complex state transitions for vehicle ordering, port processing, customization, delivery, and billing.
● Integrated with internal logistics systems and external vendor APIs.
● Designed relational data model supporting bulk orders, configurable vehicle packages, and compliance tracking.
● Implemented audit logging and reporting for operational transparency.
● Led UI/UX modernization initiative to improve workflow clarity and reduce manual processing friction.
● Created early Single Page Application front-end.
● Improved reliability and maintainability of legacy codebase while introducing modular service boundaries.
Architecture Highlights:
● Backend services built with scalable API-driven architecture.
● Complex business rule engine for pricing tiers, volume discounts, and customization constraints.
● Transaction-safe workflows for high-value fleet transactions.
● Performance optimization for large datasets and reporting queries.
Impact:
● Centralized fragmented fleet operations into a single source of truth.
● Reduced manual processing errors in bulk orders.
● Improved operational visibility for regional and executive stakeholders.
● Established architectural foundation for future integrations and automation.
Educational background
Textile Engineering (Masters Degree)
Till 1996
North Carolina State University
Polymer Chemistry (Bachelor’s Degree)
Till 1994
North Carolina State University
Languages
EnglishNative