Vetted Integration Testing Professionals

Mohithkumar Bolisetti

Screened

Mid-level Java Full-Stack Developer specializing in microservices and cloud platforms

Strongsville, Ohio5y exp

“Full-stack engineer focused on modernizing legacy financial/compliance platforms into cloud-native, domain-driven microservices. Deep hands-on experience across Spring Boot/Kafka/Redis/Postgres-Mongo backends and React/Angular frontends, with strong CI/CD and Kubernetes/OpenShift deployment practices for real-time, high-volume workloads.”

Bala Venkateswarlu K

Screened

Mid-level Data Scientist specializing in Generative AI, NLP, and MLOps

USA5y exp

“Built and deployed an LLM-powered claims-document summarization system (insurance domain) that cut agent review time from 4–5 minutes to under 2 minutes and saved 1,200+ hours per quarter. Hands-on across orchestration and production infrastructure (Airflow retraining DAGs, Kubernetes, SageMaker endpoints, FastAPI) and recent RAG workflows using n8n + Pinecone, with a strong focus on reliability, cost, and explainability for non-technical stakeholders.”

Yash De

Screened

Intern Full-Stack Developer specializing in AI/LLM applications

San Jose, CA3y exp

“Backend-focused intern who built and refactored the backend for an LLM-driven gifting mobile app using FastAPI, tackling high-latency LLM + product-API workflows. Implemented async worker-pool/queue processing with Redis caching plus retries/fallbacks, cutting end-to-end suggestion latency from ~4–5 seconds to ~1 second while improving reliability and rollout safety via staged migrations and testing.”

Harshitha K

Screened

Mid-level Full-Stack .NET Developer specializing in cloud-native microservices

Greensboro, NC5y exp

“Full-stack .NET engineer with cloud and applied GenAI experience who shipped a real-time policy status tracking module at Lincoln Financial using ASP.NET Core/.NET 8, Kafka, Angular, SQL Server, Redis, and AKS autoscaling. Also delivered a production internal LLM+RAG support assistant at Honeywell with strong security/guardrails (PII masking, RBAC) and a rigorous eval/regression loop built on a 200-question gold set.”

“Worked on a P&C insurance integration project at Accenture, using SQL to unify policy, billing, and claims data from APIs and ETL pipelines into clean reporting tables. Demonstrated hands-on experience with data quality validation, window-function-based transformations, and query performance tuning, helping business teams get a single reliable view for faster claims processing and management reporting.”

“Research-heavy ML/AI candidate with a PhD/publications background who translated LLM evaluation and clinical summarization techniques into production at ModMed. They owned an end-to-end healthcare GenAI pipeline that cut clinician documentation time from ~22 minutes to ~7-8 minutes, reduced token costs by ~30%, and built an internal evaluation framework later adopted by multiple teams.”

John Chance

Screened

Senior Machine Learning Engineer specializing in conversational AI and healthcare ML

Greenwood, LA9y exp

“ML/AI engineer with hands-on ownership of both classical recommender systems and safety-sensitive LLM agent platforms. They combine production MLOps depth with behavioral health domain experience, including clinical safety validation, explainability, and multi-agent orchestration, and cite measurable impact in both business metrics and latency reduction.”

“ML/AI engineer with hands-on experience building production systems in financial services, including a real-time underwriting analytics platform at Hartford Financial Services. Stands out for combining classic ML, low-latency API deployment, monitoring, and emerging LLM/RAG design patterns, with measurable impact including 20% better decision accuracy, sub-200ms latency, and 5M+ records processed daily.”

“Software engineer currently building AI-powered backend systems for interview analysis, with end-to-end ownership of an LLM-based monitoring platform. Stands out for combining practical product delivery in an ambiguous early-stage environment with measurable impact: over 40% reduction in manual review effort and roughly 20% lower inference cost.”

“Junior developer with over a year of experience who is already applying AI-assisted engineering practices in production, including GitHub Copilot, ChatGPT, SonarQube, and OpenAI-based workflows. On a Citibank public portal built with .NET 8 and Angular, they used AI tooling plus CI/CD quality gates to speed delivery and reportedly reduce post-deployment bugs by 30-40% while maintaining strict security and compliance standards.”

Jeffrey Ren

Screened

Mid-level Full-Stack Software Engineer specializing in SaaS and AI-enabled web applications

Henderson, NV5y exp

“Full-stack engineer who led an end-to-end rebuild of a service-agent case management app (React SPA + backend/DB updates) and added Datadog monitoring, improving agent throughput by ~1 case/hour and saving roughly $15K/month. Experienced in incremental legacy modernization (including moving a legacy React frontend toward a Rails-based approach) with heavy unit/E2E testing and strong cross-team stakeholder communication.”

KAVYA UDATHA

Screened

Mid-level Full-Stack Java Developer specializing in retail, banking, and healthcare systems

Fairborn, OH5y exp

“Full-stack engineer with hands-on ownership of a real-time retail inventory and shipment tracking platform, spanning React/Angular dashboards through Spring Boot microservices, PostgreSQL, and Kafka. Stands out for building operationally impactful systems in ambiguous environments and for thinking carefully about scalability, monitoring, and user-friendly workflow automation, even without direct production AI experience.”

Yujun Liu

Screened

Junior AI software engineer specializing in agentic workflows and cloud platforms

Cambridge, MA3y exp

“AI/full-stack builder who has owned end-to-end LLM features in both creative tooling and education workflows. Built a multi-agent film-production workspace for Backlot and an automated ingestion pipeline at iOffer.AI that cut counselor manual entry by 70%, with hands-on depth in orchestration, validation, monitoring, and human-in-the-loop system design.”

“Full Stack Software Engineer with enterprise experience across healthcare, insurance, and internal SaaS systems, combining React/TypeScript/Node.js depth with hands-on LLM feature delivery. Stands out for building secure RBAC architecture and shipping production AI workflow assistance with strong guardrails, monitoring, and human-in-the-loop decisioning in regulated environments.”

“Full-stack TypeScript engineer with hands-on experience building e-commerce and subscription/dashboard features across React, Node.js/Express, MongoDB, and PostgreSQL. Has owned features through AWS/GitHub Actions deployment and post-production support, including diagnosing stale-cache issues in production and improving team velocity by standardizing reusable UI components.”

Jaisurya Kolluru

Screened

Mid-level Software Engineer specializing in cloud-native backend and AI systems

Virginia, USA5y exp

“Full-stack engineer with hands-on ownership across React/TypeScript frontends, Node.js backends, and PostgreSQL on AWS. Stands out for production-focused database optimization, including execution-plan analysis, indexing, safe migrations, and architectural improvements that reduced database bottlenecks through a centralized REST API layer.”

“Backend engineer who built the core backend for Air Kitchens’ discovery/booking platform on AWS (Node + Python, DynamoDB, SQS/Lambda), optimizing for fast user-facing APIs and scalable async workflows. Introduced an AI matching service with a deterministic pre-filter + LLM ranking approach to balance latency vs quality, and has hands-on experience with production security (JWT/RBAC/RLS), CI/CD, and blue-green, staged migrations from Django to modular services.”

“Full Stack Engineer with hands-on experience owning customer-facing products end-to-end, emphasizing fast iteration via feature flags and risk-based testing for critical user flows. Built TypeScript/React systems with shared types and clean backend layering, and has microservices experience using RabbitMQ to decouple services and manage scale issues like queue backlogs. Also created an internal dashboard for dev/QA to centralize build/test/deploy visibility and iterated on it through lightweight user research and usage metrics.”

“AI/ML engineer with forensic analytics and healthcare claims experience (Optum), building production LLM/RAG systems to surface context-driven fraud patterns from unstructured claim notes and explain risk to investigators. Strong in large-scale retrieval performance tuning, legacy API integration with reliability patterns (SQS, circuit breakers), and MLOps orchestration on Airflow/Kubernetes with rigorous testing, monitoring, and stakeholder-friendly interpretability.”

“Full-stack engineer with enterprise experience at Metasystems Inc. (and Qualcomm) building high-traffic, security-sensitive systems—owned a secure transaction processing module end-to-end using Java/Spring Boot, Python/Django, and React. Strong AWS production operations (EKS/ECS/Lambda/RDS/DynamoDB) with IaC (Terraform/CloudFormation), observability, and reliability patterns; also delivered resilient ETL/integration pipelines with idempotency/retries/backfills and achieved a 50% deployment-time reduction through CI/CD and modular refactoring.”

“Backend engineer with experience building Spring Boot microservices for financial workflows at Fizzle (thousands of requests/minute) and shipping healthcare data validation automation at CVS Health. Demonstrates strong production reliability/performance skills—deep in database tuning (query plans, indexing, caching, denormalization), observability (Prometheus/Grafana), and resilient multi-step workflow design with retries and human-in-the-loop escalation.”

“Built and productionized an AI NL-to-SQL capability inside legacy accounts receivable software (React + Spring Boot + Postgres/pgvector RAG), adding semantic caching and a SELECT-only validation layer to satisfy infosec. Achieved measurable impact (3 days to seconds turnaround, 60% token cost reduction, 50% latency reduction) with strong adoption (40 analysts, 50+ queries/week) and documented/monitored via Confluence + logging and user feedback loops.”