Vetted API Design Professionals

Pre-screened and vetted.

HM

Junior Software Engineer specializing in full-stack AI/ML forecasting and embedded systems

San Jose, CA2y exp
Featurebox AICalifornia State University, San Bernardino
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JM

Senior Full-Stack Software Engineer specializing in AI, cloud platforms, and payments

Remote4y exp
StashleteLondon South Bank University
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JM

Executive engineering leader specializing in SaaS and automotive workflow platforms

23y exp
JM Web TechColumbia College
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DB

Executive engineering leader specializing in startup platforms, cloud architecture, and AI/Web3

Remote11y exp
GalaCalArts
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SS

Mid-level Full-Stack Software Engineer specializing in AI-powered document platforms

Jersey City, NJ4y exp
TekAssembly CorporationStevens Institute of Technology
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JD

Senior Full-Stack Engineer specializing in scalable cloud-native systems

13y exp
JS17.devTechnological University of Bolívar
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AS

Senior Full-Stack Engineer specializing in frontend architecture and scalable web platforms

India, India10y exp
GranicusMaulana Abul Kalam Azad University of Technology
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SI

Senior Full-Stack Engineer specializing in cloud-native and AI-powered systems

USA7y exp
Kunai
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BS

Senior Full-Stack Engineer specializing in Python web applications

California, USA8y exp
Lunavi
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RM

Ruthvika Mamidyala

Screened ReferencesStrong rec.

Mid-level Data Scientist specializing in GenAI, RAG, and predictive modeling

Hyderabad, India3y exp
TenXengageUniversity of North Carolina at Charlotte

Backend engineer who built and evolved Python/FastAPI services (including AWS-deployed ML prediction APIs) for real-time profitability and risk insights at TenXengage. Emphasizes pragmatic architecture, strong validation/observability, and secure access controls (RBAC + row-level filtering), and has led safe migrations via parallel runs and incremental rollouts; reports ~20% forecasting accuracy improvement.

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Snigdha Reddy Podduturi - Junior Data & AI Engineer specializing in cloud AI and analytics in Remote

Snigdha Reddy Podduturi

Screened ReferencesStrong rec.

Junior Data & AI Engineer specializing in cloud AI and analytics

Remote3y exp
Lightning MindsUniversity of Massachusetts Lowell

Built production AI backend systems in healthcare and e-commerce, including a healthcare agent that automated clinical workflows like medication refills, immunizations, and scheduling using FHIR APIs and cloud-native infrastructure. Strong in end-to-end backend ownership, LLM orchestration, and adding guardrails/validation for high-stakes and customer-facing AI workflows.

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MR

Michael Rosario

Screened ReferencesStrong rec.

Senior Backend Engineer specializing in logistics platforms and API integrations

Union City, NJ6y exp
FRAYT TechnologiesPontifical Catholic University Mother and Teacher

Backend engineer focused on reliability and production operations, with hands-on experience implementing an outbox pattern to keep database updates and downstream events consistent. They also described diagnosing transaction-related latency under heavy client traffic and using monitoring data to validate performance improvements, showing a pragmatic, systems-oriented approach to scaling integrations.

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CN

Clinton Ngotta

Screened ReferencesStrong rec.

Executive full-stack software engineer specializing in cloud platforms and FinTech

Abuja, Nigeria8y exp
SDC Startup SchoolTechnical University of Mombasa

Full-stack engineer who built a custom LMS end to end with Next.js, TypeScript, Python, AWS, and S3, owning the learning module experience and structured progress tracking. Also shipped a bilingual English-Swahili caregiver support WhatsApp chatbot using OpenAI, with strong attention to safety boundaries, anonymous logging, prompt iteration, and real-world deployment in a sensitive support context.

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Ronald Forte - Entry-Level Software Engineer specializing in AI APIs and RAG systems

Ronald Forte

Screened ReferencesModerate rec.

Entry-Level Software Engineer specializing in AI APIs and RAG systems

0y exp
RevatureHunter College (CUNY)

Junior/entry-level AI/LLM engineer who built a production-oriented RAG onboarding and knowledge assistant that ingests GitHub repos and internal sources (e.g., Confluence/Jira) using ChromaDB, with reliability features like retrieval fallbacks, retries, caching, and monitoring. Currently implementing a LangGraph-based multi-agent workflow with intent routing and Pydantic/Magentic-validated structured outputs, plus CI/CD offline evals and online metrics (Grafana/Prometheus) to improve predictability and reliability.

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SV

srinivas vuyyuru

Screened ReferencesModerate rec.

Mid-level Full-Stack Engineer specializing in AI-powered marketplace and SaaS platforms

San Francisco, CA5y exp
DoubleTree by HiltonSouthern Arkansas University

Engineer focused on real-time collaborative canvas products and AI-assisted design workflows, with hands-on ownership of both multiplayer infrastructure and LLM-powered generation systems. They combine strong TypeScript/full-stack architecture with production-grade AI evals and observability, and cite meaningful outcomes including zero sync-conflict bugs, rollout to 50,000+ DAUs, and major gains in onboarding speed and engagement.

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VJ

Mid-level Software Engineer specializing in distributed real-time systems

New York, USA4y exp
SutherlandUniversity of Central Missouri

Backend engineer focused on real-time, event-driven distributed systems (Node.js/TypeScript) with strict latency and reliability requirements. Deep hands-on experience debugging concurrency issues and designing resilient workflows (idempotency, circuit breakers, compensating actions) with strong observability; familiar with ROS/ROS2 concepts and confident ramping into robotics integrations.

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SM

Mid-level Machine Learning Engineer specializing in multimodal and time-series AI systems

6y exp
WatlowMissouri University of Science and Technology

Backend engineer who rebuilt and refactored high-traffic systems at Phenom using Java/Spring Boot/Play and also designs Python/FastAPI services. Focused on measurable reliability and performance gains through DB/query optimization, async processing, and strong observability, with disciplined rollout practices (feature flags, parallel runs, rollback) and security patterns including token auth and row-level security.

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SK

Sandeep Katna

Screened

Mid-Level Software Engineer specializing in distributed systems and AI agent workflows

5y exp
San José State UniversitySan José State University

Software engineer with enterprise CPQ/CRM/ERP integration experience (Argano) who owned an end-to-end pricing preview capability deployed on AWS Kubernetes with Jenkins CI/CD and full observability (Prometheus/Grafana). Also built an AI-native research agent using LangChain + Chroma to filter academic papers, reporting ~15 hours/week saved for a professor.

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Abdul Rehman - Senior Software Engineer specializing in Node.js/NestJS and FinTech platforms in Lahore, Pakistan

Abdul Rehman

Screened

Senior Software Engineer specializing in Node.js/NestJS and FinTech platforms

Lahore, Pakistan8y exp
Virtual ForceCOMSATS University Islamabad

Frontend engineer who led end-to-end development of the Neelum transportation admin panel (React + TypeScript + AntD), building scalable dashboards and multi-step workflows for users/roles/bookings/operational zones. Emphasizes maintainable feature-based architecture, typed API contracts, performance optimization for data-heavy UIs, and disciplined quality/rollout practices (regression checks, feature flags) in fast-paced environments.

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Hafiz Muhammad Zeshan - Senior Full-Stack Engineer specializing in MERN, AWS, and scalable SaaS platforms in Victoria, Australia

Senior Full-Stack Engineer specializing in MERN, AWS, and scalable SaaS platforms

Victoria, Australia5y exp
BunnyDoc PTY LTD.University of Engineering and Technology, Lahore

Frontend lead for B2B SaaS products, owning React + TypeScript architecture end-to-end and scaling complex dashboards/workflows with a feature-based structure, shared design system (Tailwind), and strong quality automation. Experienced shipping high-impact features quickly using incremental delivery, feature-flagged rollouts, and performance profiling/optimization to keep production stable as usage grows.

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