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Senior
Регистрация: 08.04.2026
Chris Love
Специализация: 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).
Скиллы
JavaScript/Nodejs
Open AI Integrations/RAG systems
Progressive Web Apps
Artificial Intelligence
LLM
Nodejs
RAG systems
Secure AI Platforms
Опыт работы
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.
Образование
Textile Engineering (Магистр)
По 1996
North Carolina State University
Polymer Chemistry (Бакалавр)
По 1994
North Carolina State University
Языки
АнглийскийРодной