Vetted Full Stack Developers

“Backend/data engineer with hands-on production experience across GCP and AWS: built FastAPI microservices on Cloud Run and delivered AWS Lambda + ECS Fargate systems with Terraform/GitHub Actions. Strong in data engineering (Glue/Spark, S3/Redshift) and modernization (SAS to Python/SQL), with proven reliability and incident ownership—including cutting a 20+ minute reporting query to under 2 minutes.”

“Built a personal product, Pilly AI—an AI-powered e-commerce product Q&A widget embedded via a simple script tag and served via Cloudflare CDN—covering landing page, backend, database, and deployment end-to-end. Implemented OpenAI integration with prompt/context engineering, JWT-authenticated APIs, and Postgres (NeonDB), and successfully sold the product to a client while shipping in roughly two weeks.”

“Full-stack engineer who built an internal user-activity tracking and reporting system end-to-end using React/TypeScript, Node/Express, and Postgres, deployed on AWS (EC2/ALB, S3/CloudFront) with CloudWatch observability. Emphasizes reliability and data correctness via idempotent ingestion, retries with exponential backoff, backfills/reconciliation, and performance tuning as data scales, and has experience shipping quickly in ambiguous early-stage startup conditions.”

“Product-focused full-stack engineer (70/30 app vs infra) with Accenture experience and recent AI workflow work, shipping end-to-end systems from React/TypeScript UIs through FastAPI backends to Postgres. Built an AI-driven data extraction platform with async job APIs, strict schema validation, and strong observability, and has operated AWS ECS-based deployments with real incident mitigation (DB connection exhaustion/latency under traffic spikes).”

“Open-source contributor who improved React Query’s caching/subscription behavior to reduce unnecessary re-renders via debouncing and batched updates, validated with benchmarking and extensive tests. Also maintained a Flask extension and resolved production background-task hangs by tracing Redis connection handling issues, adding cleanup/retry logic and troubleshooting docs. In a fast-paced startup, owned the design of a Celery+Redis multi-queue background processing system with Prometheus-based observability.”

“Backend engineer with strong performance and security instincts: built a Flask API for readability metrics with clean, testable modular design; optimized SQLAlchemy/Postgres to eliminate N+1 issues (800ms to 120ms). Also implemented an LLM-powered natural-language travel search using Claude Sonnet + Amadeus with RAG and anti-exploitation safeguards, plus multi-tenant isolation via Postgres RLS and Redis caching that cut search latency from ~20s to ~4–5s while reducing storage costs.”

“At HPE, led and deployed an enterprise-grade LLM document intelligence platform for an insurance client, automating extraction from highly variable PDFs/scans/emails and raising field accuracy from 74% to 93%. Built a LangChain/Pinecone/OpenSearch RAG framework to cut hallucinations by 37% and operationalized LangSmith evals in CI, driving a 41% triage accuracy lift and >33% fewer incorrect resolutions while partnering closely with claims operations via HITL workflows.”

“Backend/AI workflow engineer with experience at AirKitchenz, Uber, and Vivma Software, building production systems on AWS (Lambda, DynamoDB, Step Functions). Has a track record of major performance wins (DynamoDB latency 2s to <150ms; Postgres query 2s to ~180ms) and shipping LLM-powered onboarding and ticket-routing workflows with strong guardrails (schema validation, confidence thresholds, human-in-the-loop escalation).”

“Built and shipped production systems spanning real-time operational dashboards and an LLM-powered internal documentation assistant using RAG (embeddings + vector DB). Demonstrates strong focus on reliability and iteration: implemented guardrails and evaluation loops (human review, hallucination tracking, regression prevention) and improved performance/scalability through query optimization, caching, and retrieval tuning.”

Ashwitha Reddy

Screened

Mid-level Full-Stack Software Engineer specializing in Java/Spring microservices and AWS

Ohio, United States3y exp

“Backend/platform engineer who has owned a real-time business analytics dashboard backend (Python/Flask/MongoDB) and built Kafka event-streaming pipelines with idempotent processing and DLQs. Strong DevOps/GitOps experience deploying containerized microservices to AWS EKS with CI/CD (Jenkins/GitHub Actions/CodePipeline) and ArgoCD auto-sync/drift detection, plus hands-on support for phased hybrid cloud/on-prem migrations using feature flags and replication.”

Mukul Sai Pendem

Screened

Mid-level Full-Stack Engineer specializing in cloud-native microservices and DevOps

United States (Remote)4y exp

“Backend engineer with strong Python/FastAPI microservices ownership, including an ML-serving service with embeddings, async DB access, and Redis caching to reduce latency under high load. Experienced deploying and operating containerized services on Kubernetes using GitOps (Argo CD/Helm) with automated CI/CD, plus hands-on Kafka streaming pipeline tuning and enterprise migration work (Infosys) using blue-green/active-passive strategies.”

Arya Mane

Screened

Junior Full-Stack & AI/ML Engineer specializing in LLMs and multimodal document processing

Dallas, Texas1y exp

“Built a production RAG-based NBA player scouting assistant that embeds player profiles into FAISS, orchestrates retrieval and LLM recommendations with LangChain, and surfaces results via embedded Tableau dashboards. Demonstrates strong focus on evaluation/monitoring (batch tests, LLM-as-judge, latency/failure/token metrics) and has experience translating non-technical founder goals into DAPT + fine-tuning plans on curated data.”

“Frontend-leaning full-stack engineer with strong ownership in network observability and analytics products, including BT Group's SMARTS platform and SSpain.ai at Texas A&M. Stands out for building data-dense, near-real-time dashboards and shaping products end-to-end across React/Angular frontends, FastAPI backends, PostgreSQL, AWS, and even React Native mobile surfaces.”

“Frontend engineer with hands-on experience building data-heavy financial and climate visualization dashboards in React/TypeScript. Stands out for combining performance tuning, maintainable component architecture, and geospatial UI work, including a reported 40% reduction in initial load time on a production dashboard.”

“Frontend engineer with experience at US Bank and S&P Global building complex React dashboards for analysts working with real-time, high-volume transaction and model-performance data. Stands out for combining performance tuning, scalable state architecture, and usability improvements that directly reduced manual analysis effort for expert users.”

“Maintained and improved a set of JavaScript packages at JPMorgan, including a major refactor of an event-emission layer used in LLM streaming that delivered ~20% runtime speedup with no API changes. Known for a measurement-driven performance approach (profiling + simplification + validation), strong backward-compatibility discipline, and documentation that accelerates internal adoption and reduces support questions.”

“Backend engineer who built a containerized Flask service powering an engineering metrics dashboard by syncing GitHub and Jira data into PostgreSQL, with strong emphasis on schema design, query performance, caching, and background processing. Has hands-on experience with SaaS multi-tenancy (tenant scoping + Postgres RLS) and integrating AI/ML inference via separate model-serving services (FastAPI + TensorFlow Serving) and external APIs (OpenAI/Hugging Face/PyTorch).”

“Software engineer with Citi Bank experience building real-time fraud validation/scoring for loan processing, spanning Spring Boot microservices and a FastAPI Python service secured with OAuth2/JWT. Delivered React/TypeScript operations dashboards and deployed containerized services via Docker/Kubernetes with Jenkins CI/CD, while also tuning databases (Oracle/Postgres) and handling high-volume latency/scaling issues using ELK, caching, and autoscaling on AWS.”

“Frontend-focused engineer with experience delivering a major licensing-model migration, moving customers from legacy key-based licensing to a dynamic renewal experience. Strongest signals are in React/TypeScript architecture, reusable component design, micro frontends, and frontend quality/performance practices.”

Sanket Mungikar

Screened

Mid-level Software Engineer specializing in distributed backend and AI analytics platforms

California, USA4y exp

“Full-stack engineer at BigCommerce who combines customer-facing deployment ownership with hands-on AI/LLM systems work. Built and launched merchant analytics and predictive inventory workflows using React, TypeScript, FastAPI, Kafka, AWS, and RAG-style architectures, and has real production experience debugging non-deterministic AI issues caused by data pipeline freshness and event-ordering problems.”

“Fullstack engineer from Prudential who built a workflow automation platform for internal service reps, combining Angular/React frontends with NestJS, GraphQL, Kafka, MongoDB, and Redis. Stands out for translating ambiguous business problems into scalable metadata-driven systems, validating architecture through hands-on POCs, and delivering a measurable 40% reduction in transaction handling time.”

“Backend engineer who designed and evolved an AWS-based event-processing system in Python/PostgreSQL, achieving a 60% p95 latency reduction while improving reliability during traffic spikes. Led a zero-downtime migration from a monolithic Django app to FastAPI microservices using feature flags, strong testing, and cross-team coordination, with production-grade observability (Prometheus/Grafana/CloudWatch) and security (JWT/OAuth2, RBAC, Postgres RLS).”

“Backend engineer who built an async FastAPI data pipeline at GHN Career Academy to replace a manual Excel-based workflow, migrating 30k+ contact records into Airtable with validation/deduplication and best-effort GPT-based enrichment. Emphasizes reliability under messy real-world data and partial failures via structured logging, retries, and resumable processing, unlocking downstream automations (e.g., Zapier and chatbots).”

“Candidate indicated they live in New York but did not provide substantive details about their experience; they declined to discuss at least one topic as restricted and did not answer role-related questions with concrete examples.”