Vetted Asynchronous Processing Professionals

Dhananjay Sharma

Screened ReferencesModerate rec.

Mid-level Full-Stack Engineer specializing in AI and FinTech platforms

Stony Brook, NY6y exp

“Full-stack engineer who built RegArt’s product from 0→1 for enterprise compliance users at clients like HSBC and EY, including the production React frontend, backend APIs, and an LLM-powered search experience. Particularly compelling for startups needing someone who can move across UI, API, and data layers, make pragmatic architecture tradeoffs, and ship fast without over-engineering.”

“Backend-focused engineer with deep Java/Spring expertise in fintech and SaaS integrations, including high-scale financial data pipelines and partner-facing APIs. Most notably re-platformed a 100M+ record ETL system to a custom concurrent Spring Batch architecture that cut failures dramatically and reduced infrastructure costs by over 90%, while also leading enterprise-grade event-driven integrations for customers like Bosch and Amazon.”

“Candidate comes from SaaS and healthcare analytics rather than game development, but has strong end-to-end ownership experience building real-time, high-availability systems in Python/AWS. They highlight measurable impact across performance, throughput, uptime, and cost reduction, including queue optimization and predictive ICU utilization pipelines, and are looking to transfer that systems engineering foundation into Unity/gameplay work.”

“Cloud-focused engineer experienced deploying and stabilizing complex production systems that span APIs, infrastructure, and automated workflows, with a strong observability and safe-release mindset (feature flags/canaries/rollbacks). Has hands-on, customer-facing incident leadership, including executing DR regional failover during an AWS us-east-1 outage to maintain service and reportedly save a client ~$10M.”

“Software engineer with strong compliance-domain experience who built a customer-facing compliance and reporting dashboard using React/TypeScript with Spring Boot microservices. Demonstrates mature production engineering practices—contract-first APIs, event-driven architecture (Kafka/RabbitMQ), caching (Redis), and robust CI/CD + observability (Prometheus/Grafana/ELK)—and also created a Python-based audit automation tool adopted into the standard release process.”

“Built a production RAG-based GenAI copilot backend at Aetna using Python/FastAPI, GPT-4, LangChain, and Azure AI Search, deployed on AKS with Prometheus/Grafana observability. Owned the system end-to-end (ingestion through deployment) and improved peak-time reliability by addressing vector search and embedding bottlenecks with Redis caching, index optimization, and async processing, plus added anti-hallucination guardrails via retrieval confidence thresholds.”

“Founding engineer at a civic-tech startup (Barrow) who built and operated a Next.js/TypeScript product with map-based public reporting, including clustering and dynamic geospatial loading to improve UX and performance. Also implemented a location-aware RAG chatbot using Pinecone, web scraping/transcription, caching, and fallback web search, and owned post-launch observability plus scaling decisions (load balancing/horizontal scaling) based on API usage patterns.”

Jahnavi Bellapukonda

Screened

Mid-Level Software Engineer specializing in full-stack web and cloud systems

Remote, USA3y exp

“Full-stack engineer with strong data engineering and privacy-domain experience, having owned an automated Data Subject Rights (DSR) processing pipeline end-to-end across Azure SQL and GCP (GCS/BigQuery). Emphasizes production reliability via idempotency, validation checkpoints, structured logging/monitoring, and safe CI/CD-driven deployments, and has also built React+TypeScript + Node/Postgres web apps with scalable, maintainable architecture.”

“Built and deployed an AI-powered insurance claims fraud platform end-to-end using Java/Spring Boot, Kafka, OpenAI, pgvector, and AWS EKS. Stands out for combining LLM/RAG architecture with production-grade scalability and observability, delivering measurable impact including 62% less manual review, 40% better fraud precision, 37% higher throughput, and 99.95% uptime.”

“Python backend engineer with experience combining traditional API/microservices development and GenAI integrations, including healthcare claims workflows. Particularly compelling for teams building production AI systems: they pair hands-on work with LLMs, RAG, LangChain-style orchestration, and AWS deployment with a strong emphasis on reliability, security, and engineering discipline.”

Ming Wang

Screened

Entry-level Software Engineer specializing in AI and FinTech

Hong Kong, Hong Kong1y exp

“Recent college graduate and software engineer who relies heavily on AI-assisted development, reporting that roughly 85% of code in a recent initiative was AI-generated and then manually reviewed. Has built customer-facing AI features including personalized recommendations and an internship chatbot tied to product advertising, with exposure to API communication, database checks, and conversation monitoring.”