Vetted Java Professionals

“Unity/C# engineer with hands-on experience building cross-platform VR/mobile prototypes at Verizon Labs, including a networked VR cinema and virtual office application. Particularly strong in performance engineering: they describe custom update architecture, shader work, and low-level iOS optimization that enabled 60 FPS while rendering HD video textures and running voice chat simultaneously. They also bring adjacent computer vision integration experience from Nomad-Go, with a practical focus on latency, inference/render separation, and data consistency.”

“Backend engineer who replaced an Excel-heavy forecasting workflow with a secure, auditable FastAPI system (React UI + relational model + async workers), emphasizing deterministic processing, idempotency, and versioned ledger-style ingestion. Led a monolith-to-FastAPI migration at Bounteous using a strangler approach, feature-flagged incremental rollout, and data reconciliation/shadow-compare to protect integrity while scaling onboarding workflows.”

“Built and productionized an LLM-driven document verification workflow for a construction firm’s submittals process, moving from a Vercel/Next.js prototype to a FastAPI + LangChain/LangGraph backend with background workers and multi-server deployment. Uses LLM tools (e.g., OpenAI Codex/Cloud Code) for rapid development and log-driven root cause analysis, and partners with customer teams on evaluation metrics and iterative improvements.”

“Healthcare-focused software engineer/lead who has delivered customer-facing portals and internal call-center tools, including rebuilding a legacy Adobe Flash call center app into a modern TypeScript frontend with NgRx state management. Experienced leading onshore/offshore teams, integrating healthcare APIs, and driving adoption by visiting call centers to capture user workflows and bake them into regression testing—work that proved especially valuable during COVID-era shifts to video appointments.”

Reza Mawardana

Screened ReferencesStrong rec.

Mid-level Software QA Engineer specializing in Insurance and HRIS, transitioning to SDET

7y exp

“QA-minded candidate with unusually deep, self-driven experimentation in game mechanics (RNG and EXP optimization) using emulators and save-state methodology, translating that same rigor into production release verification and bug triage practices. Has experience in embedded QA and proactively proposed a centralized QA model with sharing sessions to improve coverage during absences; uses AI tools to speed up Jira documentation and test case creation.”

Vaishnavi Kashyap

Screened ReferencesStrong rec.

Junior Full-Stack Software Engineer specializing in web platforms and sustainability analytics

1y exp

“Full-stack/backend engineer who owned a production digital assembly planning platform at Saint Gobain end-to-end (React/Node/Postgres), maintaining 99.9% uptime across 5 factory sites and driving a reported 90% improvement in factory-floor coordination. Also built and operated BigQuery + Vertex AI (ARIMA) forecasting/data pipelines processing 1M+ datapoints daily, with strong emphasis on idempotency and data-quality validation to prevent incorrect outputs.”

“Built and deployed an AI Copilot for Healthful Telehealth that helps dietitians generate personalized meal plans using patient data and real-time clinical context. Stands out for owning the full lifecycle—from workflow discovery and ETL/RAG architecture to production incident response and post-launch stabilization—while delivering roughly 30% gains in retrieval accuracy and latency.”

“Built a production LLM-powered fraud detection platform at Wells Fargo, combining OpenAI/Hugging Face models with RAG-based explanations to make flagged transactions interpretable for risk and compliance teams. Delivered low-latency, real-time inference at high scale on AWS (SageMaker + EKS), with strong observability and security controls, reducing manual reviews and false positives in a regulated environment.”

suparshwa patil

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in AI platforms and full-stack systems

Santa Clara, CA4y exp

“Built and shipped a production AI-powered Q&A/RAG onboarding assistant at One Community Global that unified knowledge across Notion, Google Docs, and Slack, cutting volunteer onboarding time by 45%. Demonstrates strong end-to-end ownership: LangChain agent orchestration integrated into a FastAPI backend, rigorous evaluation (200-query dataset, ~85% accuracy), and production feedback/monitoring with source-attributed answers to build user trust.”

“Internship experience building an end-to-end medical AI pipeline that extracts and normalizes messy medical PDFs, fine-tunes BioBERT to classify tumor-related statements (including negation/ambiguity handling), and integrates image-model outputs (MedSAM/GroundingDINO) for tumor localization and classification. Also worked on an LLM/RAG system to draft IPO prospectuses using retrieved regulatory/financial sources (including SEC EDGAR) with structured prompts to reduce hallucinations.”

“Built and launched an internal AI troubleshooting assistant focused on safe, retrieval-first root cause analysis for enterprise systems, with strong attention to monitoring, fallback behavior, and post-launch iteration. Also owns full-stack product work across React and Java/Spring Boot, including high-volume financial operations workflows, and reports measurable LLM improvements such as ~30-40% latency reduction.”

“Backend engineer currently at Citigroup working on real-time transaction processing systems with Kafka. Stands out for using AI tools pragmatically in a regulated banking environment to improve debugging, testing, and developer productivity while keeping human control over architecture, security, and performance decisions.”

“Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.”

“Backend engineer with experience building and modernizing high-volume healthcare transaction systems, including migrating Java services to Spring Boot microservices and adopting Kafka-based event-driven architectures. Strong focus on production reliability and operability (observability, CI/CD, standardized patterns) plus security (OAuth/JWT, RBAC, Postgres/Supabase RLS) and resilient stream processing (idempotency, DLQs).”

“ML/AI practitioner in healthcare (Syneos Health) who has deployed production clinical NLP and risk models. Built a BERT-based physician-note information extraction system on Docker + AWS SageMaker (reported ~42% retrieval improvement) and automated retraining/deployment with Airflow and drift detection, while partnering closely with clinicians to drive adoption (reported ~18% readmission reduction).”

“Built and productionized a tool-first, multi-agent framework that augments an anomaly detection model with domain context to generate trustworthy, evidence-backed anomaly explanations (including false-positive likelihood). Architected the platform to be model/orchestration/vectorDB agnostic (e.g., GPT + CrewAI + ChromaDB vs Claude + LangGraph + other vector DB) with strong performance, reliability, and OpenTelemetry-based observability. Also built a personal LangGraph-based "mock interviewer" agent that asynchronously fuses voice + live code input using state reducers, stop conditions, and fallback routing.”

“Software engineer with strong full-stack and platform experience (TypeScript/React/Node.js) who has built real-time analytics dashboards and microservices using RabbitMQ. Demonstrates production-minded decision-making under launch pressure (manual fallback for payment-impacting third-party API issues) and has delivered internal DevOps tooling that automates compliance checks via GitHub/Jira integrations.”

“Master’s student and Deep Learning teaching assistant who teaches LLM/VLM fine-tuning (including LoRA) and built a Hugging Face LLM fine-tuned for unit conversion, improving reliability by analyzing synthetic data and filling missing number-system conversion examples. Also implemented the Raft consensus protocol using gRPC in a distributed systems course with correctness validated by unit tests.”

“AI/ML infrastructure-focused engineer who has built production RAG systems from scratch (Supabase/pgvector + OpenAI embeddings) and iterated using formal eval metrics to improve retrieval quality. Also debugged real-time audio issues in a LiveKit-based pipeline by correlating packet loss with VAD behavior, and has deep experience building brittle, customer-specific financial platform integrations in Python/Playwright (2FA, redirects, token refresh, rate limits).”

“Full-stack engineer/product owner who built and scaled a customer-facing job application portal (Skillbridge) using TypeScript/React and Spring Boot/MongoDB, optimizing search performance with indexing, caching (Redis), and payload/lazy-loading improvements. Also built an internal AI-driven analytics dashboard for Salesforce operations using OpenAI sentiment analysis, achieving 70% reduction in manual analysis and driving adoption through demos and iterative feedback.”

“Full-stack engineer who built and owned an internal analytics dashboard for sales (React/TypeScript + Node/Express + NoSQL), delivering it two weeks early with zero production issues and a reported 10% sales-efficiency lift. Experienced with microservices and async messaging patterns (retries/DLQs/idempotency), and emphasizes rapid iteration with strong CI/CD and automated testing plus user-driven adoption.”

“Cloud-native integration engineer (Oracle/OCI) with strong production deployment and incident-response experience, including API gateway rollouts, observability (Prometheus/Grafana), and multi-layer debugging for payments systems. Built Python/FastAPI microservices and automation for customer-specific reporting and data sync, and has delivered major performance gains (45 min to <10) plus reliability improvements (MTTD reduced 40%+) through monitoring, playbooks, and resilient integration patterns (streaming/queuing, retries, secure tokens, VPC peering).”