Vetted Webpack Professionals

“Backend engineer with deep experience modernizing a provider credentialing/compliance platform with multiple upstream/downstream integrations. Focused on building predictable, scalable REST APIs (primarily ASP.NET Core; framework-agnostic approach applicable to FastAPI), improving performance via DB/query optimization, and hardening workflows with idempotency, transactions, feature flags, and strong auth/RBAC patterns.”

“Backend/data engineer with hands-on AWS serverless and data platform experience: built a Lambda-based reCAPTCHA/token validation + SES subscription workflow and designed Glue ETL pipelines producing Parquet datasets for Athena. Also led a high-traffic multilingual media CMS modernization at Radio Free Asia by migrating to Arc XP using parallel runs, phased rollouts, feature flags, and rollback plans.”

“Backend-focused engineer who owned an AI mentoring workflow platform built in Django with LangGraph multi-agent orchestration, optimizing it to stay under 200ms latency while scaling past 1,200 active users using profiling, caching, load testing, and OpenTelemetry-style tracing. Also has hands-on experience containerizing and deploying Python/ML services to AWS ECS via GitHub Actions/GitOps, and building reliable real-time pipelines with webhooks and Redis queues (idempotency, backpressure, DLQ).”

“Full-stack engineer focused on data-heavy platforms, building Spring Boot microservices and Angular/React dashboards end-to-end. Has hands-on experience improving large-scale API and UI performance (including cutting 8–10s response times) and ensuring cross-service consistency using Kafka, idempotent consumers, and strong validation/transaction patterns on AWS with CI/CD and observability (Prometheus/ELK).”

Ravinder Reddy Mamidi

Screened

Mid-Level Full-Stack Engineer specializing in Java Spring Boot and React

Remote, US3y exp

“Full-stack engineer who built a cloud-native customer servicing platform at Synchrony using React 18/Next.js (SSR) and Spring Boot microservices on AWS. Experienced with high-volume, event-driven systems (Lambda/SNS/SQS) and strong distributed-systems rigor around data integrity (idempotency, DynamoDB conditional writes) plus production-grade security/observability (JWT/OAuth2, WAF, Actuator, Splunk).”

“Backend/data engineer with hands-on production experience building a real-time notification API on Flask/Celery/Postgres and scaling it on AWS with Docker, Redis queuing, and SQLAlchemy query optimization. Also delivered AWS serverless deployments (Lambda) using Terraform + GitHub Actions and built AWS Glue ETL pipelines from S3 to Redshift with CloudWatch monitoring and DataBrew data quality checks.”

“Backend/data engineer with hands-on production experience across Python/Flask microservices and AWS serverless/data platforms (Lambda, DynamoDB, S3, Glue/PySpark). Demonstrated strong reliability and operations mindset (JWT/RBAC, retries/timeouts/circuit breakers, CloudWatch/SNS alerting) and measurable performance wins (SQL report runtime cut from 10 minutes to 30 seconds). Seeking ~$150k base and cannot travel for onsite meetings for the next 5–6 months due to family medical constraints.”

“Frontend engineer focused on high-scale React + TypeScript dashboards, including an internal Instagram creator/agency analytics dashboard handling extremely large datasets (1–2TB) with virtualization and performance profiling to maintain ~60fps UX. Experienced in modern state management (Redux Toolkit/RTK Query), modularizing legacy codebases into shared component libraries (Storybook), and shipping fast with feature flags plus automated QA (Playwright/Selenium).”

Ruslan Labza

Screened

Mid-level Full-Stack Engineer specializing in modern web applications

San Francisco, CA6y exp

“Built and launched a production AI chat assistant inside a data processing platform, focused on helping users understand large table outputs and job results faster. Brings strong end-to-end product engineering across React/TypeScript frontend, backend APIs, and LLM integration, with a clear emphasis on reliability, safe behavior, and iterative quality improvements after launch.”

“Frontend engineer with enterprise B2B and healthcare web experience, specializing in React/TypeScript architectures for complex, permissioned workflows and data-heavy admin dashboards. Has modernized legacy codebases with improved state management (Redux/Zustand/Rematch, IndexedDB caching) and shipped fast Next.js SEO/performance-focused builds under tight deadlines, including measurable bug-ticket reduction via improved error reporting.”

“Full-stack/ML-leaning engineer who has shipped production-grade real-time analytics and an internal AI support assistant using RAG over enterprise documentation. Demonstrates strong systems thinking across scalability, reliability, observability, and LLM safety/evaluation (thresholded retrieval, RBAC, response validation, regression-gated evals), with concrete iteration based on performance metrics and user feedback.”

“Full-stack/AI engineer who has shipped production systems spanning real-time analytics dashboards and an internal LLM-powered knowledge assistant. Experienced with RAG pipelines (embeddings/vector DB, semantic retrieval, query rewriting) plus evaluation loops and guardrails, and builds observable Kafka-based data pipelines monitored with Prometheus/Grafana.”

“Full-stack engineer currently building an employee management system end-to-end with React, Node/Express, and PostgreSQL, including JWT auth and RBAC. Previously worked at TCS on large-scale State Bank of India web applications, applying Redis caching, server-side pagination/filtering, and async job offloading to improve performance and reliability.”

Anita Bhagashetti

Screened

Mid-Level Software Engineer specializing in distributed systems and cloud microservices

3y exp

“Built and productionized a RAG-based semantic search system for video-derived data, focusing on measurable success metrics (p95 latency, reliability, cost/request) and strong observability (prompt versions, retrieved docs, tool calls, token usage). Experienced in diagnosing real-time issues in LLM/agentic workflows and in supporting go-to-market efforts through tailored technical demos, rapid POCs, and post-close onboarding.”

“Built a production 13-step LLM agent pipeline at Sentari AI that converts diary entries into personalized AI responses and a persistent structured user profile. Stands out for a pragmatic approach to production AI: strict schemas, function calling, state-machine orchestration, confidence thresholds, logging, and validation layers to control non-determinism and prevent bad profile updates.”