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Keyword Target: AI workflow automation engineer

AI Workflow Automation Engineer for Product Operations

I build applied AI workflows for real products where correctness and operations matter more than demos. The goal is dependable automation with human oversight where needed.

Ideal For

  • Teams automating document-heavy business processes
  • Products that need AI output verification workflows
  • Organizations integrating AI into existing operational systems

Core Capabilities

  • Design AI extraction and classification pipelines for unstructured data
  • Build HITL interfaces for validation and exception handling
  • Integrate AI outputs into type-safe product workflows
  • Track quality metrics and optimize model-assisted operations

Outcome Evidence

  • Delivered AI extraction workflows with 94% accuracy in production
  • Tripled operational throughput on document digitization pipelines
  • Reduced manual administrative effort in high-volume workflows
TypeScriptNext.js 16Ruby on RailsZodPostgreSQL

Related Projects

AppEase

Modernized legacy insurance platform with Next.js frontend, engineered PDF-to-Form digitization engine, and built Human-in-the-Loop AI platform for underwriters.

Next.js 16Ruby on RailsTypeScriptTailwind CSSPostgreSQL
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DevDen

Cross-platform React Native application used by 700+ property managers globally for real-time booking management.

Next.jsReact NativeRuby on RailsTypeScriptTailwind CSS
Visit project

Relevant Experience

DataCrest

Philadelphia, United States

Full Stack Engineer: Modernized the stack across 3+ major projects, migrating legacy Bootstrap 3 monoliths to a utility-first Tailwind CSS architecture, which wiped out years of technical debt and CSS bloat. Decoupled the Rails monolith into a headless backend architecture, serving a high-performance Next.js 16 frontend to improve scalability and boost developer velocity. Deployed a unified Design Language and UX framework using Tailwind CSS and Radix UI, standardized user experience across products to reduce friction in core workflows and decrease the number of interactions needed to complete tasks. Engineered an end-to-end PDF-to-Form digitization engine that eliminated manual data entry; this system used a bi-directional sync to convert complex insurance docs into interactive forms and back, tripling the team's operational throughput. Architected an AI-driven data extraction layer that parsed unstructured PDF data into structured JSON, allowing the system to automatically analyze and route insurance data to external partners with 94% accuracy. Built a "Human-in-the-Loop" (HITL) AI platform for underwriters to verify AI-extracted data through a custom dashboard, which drastically accelerated the review process for high-risk applications. Developed a real-time insurance orchestration engine that dynamically calculates risk and generates instant quotes for prospects based on complex, multi-factor insurance logic. Implemented a comprehensive Design System using Storybook, organizing UI components into a version-controlled library that ensured 100% visual consistency across all different products both internal and external facing. Scaled the platform to support the onboarding of Sapiens, an enterprise-level provider, successfully managing the integration of hundreds of underwriting agencies into a single unified system. Digitized hundreds of industry-standard ACORD forms using internal AI models, turning static legacy documents into type-safe, validated digital workflows.

DevDen

Faisalabad, Pakistan

Software Engineer: Re-architected a legacy Ruby on Rails UI by migrating from Bootstrap to a custom Tailwind CSS framework, slashing page load times by 90% (from 3s to <300ms) and significantly improving Core Web Vitals. Managed the core upgrade of Ruby on Rails monolith from version 5 to 7, handling the full transition of the asset pipeline and middleware to support modern concurrent Ruby features. Engineered a secure Next.js 16 orchestration layer to proxy a Ruby on Rails API, obfuscating backend architecture from adversaries while leveraging PPR and Streaming to deliver a high-performance, near-instantaneous user experience with 100% end-to-end type safety. Developed a schema-driven UI engine using Zod and TanStack Query to dynamically generate complex forms from unified JSON metadata, eliminating redundant Form.io configurations and accelerating internal tool deployment by 70%. Architected an automated data synchronization pipeline to replace manual daily syncs, reducing data latency from 24 hours to near real-time and eliminating revenue loss caused by stale rental inventory and reservation conflicts. Engineered a cross-platform React Native application utilized by 700+ property managers globally, centralizing real-time booking management and support ticket workflows to drastically improve on-site operational agility. Led the push for better guest data by conducting discovery sessions with property managers, implementing OpenGDS integrations and occupancy-sync services that eliminated double-bookings and resolved 100% of data discrepancies in guest metadata. Architected an end-to-end HR Management System (HRIS) featuring AI-driven workflows that automated high-volume candidate onboarding, reducing administrative task time by 85% by transforming manual role assignments into a one-click process. Designed a highly extensible HR architecture supporting bespoke employee lifecycles and business logic, delivering a data-rich dashboard that optimized assessment completion rates for employees while providing stakeholders with granular analytics.

Related Expertise Pages

FAQ

Do you build AI features with human validation?

Yes. HITL review flows are central when AI outcomes need accuracy and auditability.

Can AI workflows integrate with legacy systems?

Yes. I typically use adapter layers and typed contracts to bridge legacy and modern systems.

How do you measure AI workflow success?

I track extraction accuracy, review throughput, and operational time saved as core metrics.

Looking for help with this stack?

Reach out with your project context, current constraints, and delivery goals.

Contact Kawish