Gurudatt Gaonkar
Software Developer
5+ years exp
5+ years ML exp
3+ projects
Node JS
Java
Python
TypeScript
JavaScript
Accessibility & Semantics: 4/10
UX & Visual Polish: 4/10
Active 2 days ago
+1 (857) 3951690
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Overview
Technical skills
Timeline
Roles
Overview
Full-stack TypeScript developer at a middle level specializing in backend orchestration and web application prototypes. The strongest proven skill is building AI-driven backend orchestration and processing pipelines as shown in apps/backend/src/services/triage.service.ts and apps/backend/src/services/groq.service.ts. There is little to no public evidence of automated test suites, CI test integration, or formal QA practices in the analyzed human-authored files.
Technical skills
Node JS• Middle
Java• Junior • 5y+ • 2 projects
Python• Middle • 4y+
TypeScript• Middle • 4y+ • 3 projects
JavaScript• Senior • 4y+ • 3 projects
Node JS
Express
Prisma
Axios
Python
Django• 4y+
Django REST Framework• 4y+
Databases
PostgreSQL• 5y+
MySQL• 4y+ • 2 projects
Frontend
React.js
Tailwind CSS
Vite
React Query
Zustand
Zod
DevOps
Rest API
AWS• 4y+
AI/ML
Computer Vision• 5y+ • 1 project
LLM• 1 project
AI Agents• 1 project
Timeline
Northeastern University
Master's Degree •
Computer Science
• Architected DevBoard, a full-stack developer community platform with session-based authentication via Passport.js
• Owned feedback and bookmark modules: 5-star ratings, sorting, and an accessible modal for a Lighthouse score of 100
React.js
Node JS
Express
MongoDB
TypeScript
Software Engineer
•
Middle
Duck Creek Technologies
•
Full-Time
• Owned end-to-end development of a SaaS insurance Claims product built on Python, Django, Django REST Framework, PostgreSQL, and AWS, shipping 17+ features annually for 8 enterprise clients generating $25M+ in ARR
• Optimized the payment release workflow across microservices by designing new REST APIs and refactoring call patterns to eliminate over-fetching, cutting workflow load time by 32%
• Migrated system and admin authentication from password-based credentials to OAuth 2.0 via AWS Secrets Manager, replacing 100% of static credentials and reducing credential-exposure risk
• Built automated Selenium and TypeScript test suites into a Jenkins CI/CD pipeline, expanding application test coverage by 60% and catching regressions before they reached production
• Containerized and shipped 12+ production releases and hotfixes with Docker and Kubernetes in an Agile/Scrum environment, sustaining zero major rollbacks across deployments
• Maintained 24/7 on-call support for severity-1 incidents with a 15-minute average response time, debugging and resolving 40+ high-severity defects annually to keep production stable
Python
Django
Django REST Framework
Docker
Kubernetes
AWS
University of Mumbai (Bombay)
Bachelor's Degree •
Computer Science Engineering
• Crafted Samvaad, a web application that converts English speech into Indian Sign Language for hearing-impaired users
• Designed NLP pipeline (Web Speech API + syntactic parsing) translating English to ISL syntax with 500+ sign gestures
NLP
NLTK
WebSockets
Full Stack Development Intern
•
Junior
The Sparks Foundation
• Engineered a full-stack CRM platform with Java, Spring Boot, and React spanning 6 core modules and 30+ REST endpoints to manage the complete sales lifecycle from lead capture to customer conversion
• Implemented JWT-based authentication with Spring Security and BCrypt password hashing, enforcing role-based access control across 3 user roles
• Modeled a normalized PostgreSQL schema with JPA/Hibernate mappings and DTO-mapped services, ensuring data integrity across the platform’s relational entities
• Built a lead-to-customer conversion flow and dashboard visualizing revenue, pipeline, and task metrics with Chart.js
Java
Spring Boot
React.js
PostgreSQL
• Built a computer-vision pipeline (PoseNet, TensorFlow + ml5.js) matching webcam pose to references at <200ms latency
• Applied cosine-rule joint-angle math across 17 skeletal keypoints to score posture, flagging bad form at 92–98% accuracy
• Rendered color-coded skeletal overlays for live form correction, running full inference in-browser with zero install
TensorFlow
Computer Vision
Middle Frontend Developer
Confidence: Medium Fullstack
Fullstack web developer (middle level) focused on building pragmatic React frontends and Node/TypeScript backends with good separation of concerns and operational workflows. The strongest proven skill is implementing end-to-end AI orchestration and server-client integration as shown by the triage service and related controllers (ai-email-triage-assistant/apps/backend/src/services/triage.service.ts and apps/backend/src/controllers/email.controller.ts). There is limited evidence of automated tests, CI-driven quality gates, measured performance optimization, or advanced state machines in the public code.
UI Component Architecture
3/10
How interface parts are built
Reasonable component decomposition and modular React components, but no evidence of a custom design system, advanced composition patterns, or a shared token system authored by the developer.
Evidence
DevBoard/frontend/src/components/ProjectList.js
DevBoard/frontend/src/components/ProjectCard.js
DevBoard/frontend/src/App.js
Responsive & Cross-browser
3/10
Works on all screens and browsers
Responsive layouts and media queries are implemented with CSS; patterns are conventional (media queries, fluid containers) rather than advanced (container queries or internationalization feature detection).
Evidence
DevBoard/frontend/src/App.css
ai-email-triage-assistant/apps/frontend/src/App.css
CampusRecover/public/found.html
Performance Optimization
2/10
Speed of the interface
Some pragmatic performance and concurrency considerations (batching, pagination) are present, but no measured optimizations, bundle analysis, virtualization, or deliberate code-splitting artifacts are evident.
Evidence
ai-email-triage-assistant/apps/backend/src/services/triage.service.ts - BATCH_SIZE and Promise.all batching
DevBoard/frontend/src/components/ProjectList.js - server pagination and LIMIT usage
ai-email-triage-assistant/scripts/generate-sample-dataset.ts - dataset generation for load testing
Accessibility & Semantics
4/10
Usable for everyone
Concrete accessibility work is present: ARIA attributes, sr-only headings, visible focus styles, and keyboard handling for custom UI elements, demonstrating awareness of basic a11y needs.
Evidence
DevBoard/frontend/src/App.js - aria-haspopup, aria-expanded and Escape key handling for menus
DevBoard/frontend/src/components/ProjectList.js - sr-only heading and semantic section/aria-label usage
DevBoard/frontend/src/App.css - focus-visible styles
State Management & Data Flow
3/10
Managing data in the app
Clear separation of concerns between controllers/services and client-side API adapters; server-side batching and error-state updates show server-state discipline, but advanced patterns like optimistic updates with rollback, request cancellation, or explicit state machines are not present.
Evidence
ai-email-triage-assistant/apps/backend/src/services/triage.service.ts - triageEmail, processSingleEmail, processPendingEmails with error handling
ai-email-triage-assistant/apps/frontend/src/api/emailApi.ts - uploadCsv with progress and explicit error parsing
DevBoard/frontend/src/context/UserContext.js - centralized user state
UX & Visual Polish
4/10
Look and feel quality
Good UX hygiene: loading, empty and error states, confirmations, and visual polish are consistently implemented across pages and components, but there is no evidence of A/B testing or measured perceived-performance experiments.
Evidence
DevBoard/frontend/src/components/FeedbackList.js - loading and empty states, pagination
DevBoard/frontend/src/components/ProjectList.js - loading, no-results and clear-filters UI
CampusRecover/public/js/modules/foundItems.js - user-facing validation, empty states, and UX feedback
Expertise
React• Middle
HTML & CSS• Middle
Frontend Architecture & Build Tools• Middle
Industries
Education• Middle
Software• Middle
Technologies
Node JS• Middle
Zustand
Tailwind CSS
Express
Prisma
React.js
Vite
React Query
Axios
Zod
Recommendations
- Lead development of internal web apps or APIs that combine React frontends with Node/TypeScript backends (dashboard tools, admin consoles, internal automation UIs).
- Implement AI orchestration prototypes or integrations where LLM outputs drive backend workflows and tool-calls, including mock execution and retryable pipelines.
- Build feature-rich SPA pages with pagination, filtering and accessible components, and own the end-to-end client-server contract using Prisma or similar ORMs.
- Expand into production hardening tasks: add automated tests, CI pipelines, and measurable performance reviews (bundle analysis and RUM) to increase reliability and scalability.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle QA Engineer
Confidence: Medium Generalist
Full-stack TypeScript developer at a middle level specializing in backend orchestration and web application prototypes. The strongest proven skill is building AI-driven backend orchestration and processing pipelines as shown in apps/backend/src/services/triage.service.ts and apps/backend/src/services/groq.service.ts. There is little to no public evidence of automated test suites, CI test integration, or formal QA practices in the analyzed human-authored files.
Test Automation Frameworks
Building automated tests
Not evidenced in public code
Test Coverage & Strategy
What and how to test
Not evidenced in public code
API & Integration Testing
Testing how parts work together
Not evidenced in public code
Performance & Load Testing
Testing speed under load
Not evidenced in public code
Bug Reporting & Analysis
Finding and describing bugs
Not evidenced in public code
CI Test Integration
Running tests automatically
Not evidenced in public code
Industries
Education• Middle
Software• Middle
Technologies
TypeScript• Middle • 4y+ • 3 projects
Rest API
Recommendations
- Develop backend orchestration, retryable pipelines, and data ingestion services - extend the triage.service.ts pattern into production-grade job handling with queued workers.
- Build API-first features and robust API contracts - add schema validation and contract tests for endpoints defined in controllers (e.g., email.controller.ts).
- Add test automation and CI - introduce unit and integration tests, API contract checks, and a CI workflow that captures artifacts and run selective test matrices.
- Implement observability and SLOs for the orchestration pipeline - add structured logging, metrics, and simple load tests to validate scale assumptions.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Junior Backend Developer
Confidence: Medium API Engineer
Full-stack API engineer (Middle) specializing in building TypeScript/Node.js backend services that orchestrate AI model calls. The strongest proven skill is implementing LLM-backed orchestration and triage pipelines as shown by apps/backend/src/services/groq.service.ts, apps/backend/src/services/triage.service.ts and apps/backend/src/controllers/email.controller.ts. There is limited public evidence of production-grade schema migrations, transactional multi-statement safeguards, formal test coverage, distributed service design, or hardened authentication and secrets management.
API Design
5/10
How well APIs are designed
Practical, consistent REST endpoints and error contracts with client-side API helpers; lacks formal versioning, pagination at the API layer, idempotency keys, and documented error schemas.
Evidence
ai-email-triage-assistant/apps/backend/src/controllers/email.controller.ts: consistent {success,message,data} JSON responses and endpoints (/emails, /emails/process, /emails/:id/process, /emails/:id/retry)
ai-email-triage-assistant/apps/frontend/src/api/emailApi.ts: client-side API wrapper and error handling for the backend endpoints
ai-email-triage-assistant/apps/backend/src/routes/upload.routes.ts: upload endpoint with multer (upload handling evident)
Data Layer & Database
4/10
Working with databases
Uses Prisma for data access with clear domain operations and status transitions; missing explicit transaction boundaries, migration history, tuned/raw SQL, or explicit handling of multi-statement atomicity.
Evidence
ai-email-triage-assistant/apps/backend/src/services/triage.service.ts: creates toolCall rows and updates email.processingStatus without explicit transactions
ai-email-triage-assistant/apps/backend/src/controllers/email.controller.ts: findMany with include for toolCalls->execution and updates to processingStatus
ai-email-triage-assistant/apps/backend/src/services/csv-ingestion.service.ts: Prisma usage during CSV ingest (data-layer integration)
Scalability & Performance
4/10
Handling load and speed
Shows basic scalability thinking: batching, parallel processing and LLM input truncation; lacks queue/worker infrastructure, cache-invalidation strategy, connection-pool tuning, or measured performance artifacts.
Evidence
ai-email-triage-assistant/apps/backend/src/services/triage.service.ts: BATCH_SIZE constant and Promise.all over batches for concurrent processing
ai-email-triage-assistant/apps/backend/src/services/groq.service.ts: truncates LLM input (truncatedBody) to control request size
ai-email-triage-assistant/scripts/generate-sample-dataset.ts: dataset generation script demonstrating data-volume handling
System Architecture
4/10
Overall system structure
Clear modular structure with controllers, services, routes, utils and monorepo workspaces; design is a single cohesive API service rather than a multi-service distributed system and lacks documented inter-service contracts or graceful degradation patterns.
Evidence
ai-email-triage-assistant/apps/backend/src/app.ts: Express app wiring and route imports
ai-email-triage-assistant/package.json: workspace layout (apps/*) showing monorepo organization
ai-email-triage-assistant/apps/backend/src/controllers/email.controller.ts: separation of controller and service responsibilities
Security & Auth
2/10
Protecting data and access
Basic security hygiene present (env var usage, truncation of inputs), but minimal evidence of authentication/authorization, input validation at boundaries, secrets management, or dependency audit and no obvious protection against injection/SSRF beyond naive measures.
Evidence
ai-email-triage-assistant/apps/backend/src/services/groq.service.ts: uses process.env.GROQ_API_KEY and truncates body before sending to LLM
ai-email-triage-assistant/apps/backend/package.json: dotenv listed as dependency (env management present)
ai-email-triage-assistant/apps/backend/src/routes/upload.routes.ts: multer used for file uploads (upload surface present but no shown validation middleware)
Reliability & Observability
4/10
Stability and monitoring
Good practical error handling, logging, explicit retry/reset endpoints and LLM parse error handling; lacks advanced reliability patterns such as timeouts with backoff, circuit breakers, structured correlation ids across requests, and metrics/alerts examples.
Evidence
ai-email-triage-assistant/apps/backend/src/controllers/email.controller.ts: try/catch handling, status updates, and retry endpoint that resets processing state
ai-email-triage-assistant/apps/backend/src/services/triage.service.ts: per-email error handling and batch processing with logging
ai-email-triage-assistant/apps/backend/src/services/groq.service.ts: explicit handling of parse errors and rate limit errors with descriptive messages
Verified artifacts
Expertise
Backend AI & LLM• Junior
Java• Junior
Node.js• Junior
Industries
Software• Middle
Artificial Intelligence• Junior
Technologies
Java• Junior • 5y+ • 2 projects
Recommendations
- Develop LLM orchestration and AI-to-business workflows (tool-call orchestration, model response parsing) leveraging the existing groq.service.ts and triage.service.ts patterns.
- Build resilient worker/queue-based processing (e.g., background job queue with visibility/timeouts) to replace Promise.all batch concurrency for large-scale email ingestion.
- Harden data-layer operations by introducing explicit DB transactions for multi-step writes and a migration history (Prisma migrations) around triage/tool-call flows.
- Implement API-level improvements: versioning, pagination, idempotency keys for processing endpoints and structured error schemas.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: