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Vetted Software Engineers
Pre-screened and vetted.
Bhanu Gummadi
Mid-level Backend Software Engineer specializing in cloud-native microservices and FinTech
“Backend-focused engineer with Mastercard experience building and operating high-volume transaction-processing microservices. Has owned customer-facing banking services end-to-end and built an internal on-call analytics tool that centralized logs/metrics with real-time filtering to speed root-cause analysis and reduce incident investigation time.”
“Designed and deployed a production LLM agent platform at the National Institutes of Health to reduce time spent searching fragmented internal documentation, combining RAG grounding with multi-step tool-calling workflows and integration into legacy services via inference APIs. Emphasizes production-grade reliability through automated evaluation on real queries, guardrails/safe-failure behaviors, and ongoing A/B testing and monitoring, and has experience translating non-technical stakeholder goals into measurable success metrics.”
Gary Chang
Senior Full-Stack Software Engineer specializing in AI-first cloud-native systems
“End-to-end engineer who has productionized AI automation and RAG capabilities, building full-stack systems (React/Node/Redis/Postgres + vector DB) with evaluation-driven quality gates and monitoring. Reported ~60% reduction in manual ops time and major turnaround improvements, and has experience modernizing legacy systems safely via feature flags and parallel runs while working across product, data, and ops teams (System1).”
Pranita Agrawal
Mid-level Software Engineer specializing in Java microservices and AWS
“TypeScript backend/full-stack engineer who owned an internal business workflow platform end-to-end in production, including API/data design, relational DB integration, and enterprise integrations. Has hands-on experience operating workflow processing services with Kafka-style event-driven patterns, idempotency, exponential backoff retries, dead-letter queues, and strong observability, plus API design with OpenAPI/Swagger and token-based auth.”
Nithish Nelagal
Mid-Level Software Engineer specializing in data engineering and cloud platforms
“Backend-leaning full-stack engineer who has shipped production-critical data/reporting features at Walmart and built an end-to-end workflow automation product (FastAPI + React/TypeScript + PostgreSQL) deployed on AWS. Strong in performance/reliability engineering (parallel ETL, batch DB operations, indexing via EXPLAIN ANALYZE), secure API design (JWT/RBAC), and pragmatic incident-driven scaling (separating workers from API layer).”
Sadiya Bhawania
Senior Software Engineer specializing in AI and full-stack web platforms
“Full-stack TypeScript engineer with early-stage startup experience (HomePulse; sole US engineer) who ships and owns production features end-to-end—routing/state design, API contracts, caching/pagination, and post-launch monitoring/optimization. Has delivered performance-sensitive React UIs (virtualized large datasets, React Query caching, Suspense loading patterns) and built durable job-queue workflows with idempotency/retries, plus SQL Server relational modeling for internal ticketing and knowledge-retrieval workflows.”
Ganesh Pittala
Senior Software Engineer specializing in distributed systems and cloud-native platforms
“Backend-leaning full-stack engineer with experience at Walmart, Qualtrics, and American Express, shipping secure partner-facing API platforms and internal monitoring dashboards. Strong in AWS production operations (ECS/Fargate, RDS/Postgres, CloudWatch) plus rigorous testing/security practices, with measurable delivery and performance improvements (35% faster releases; ~30–40% latency reductions).”
Aaron Bahr
Senior Software Engineer specializing in game engine and systems programming
“Unity/C# developer who built highly flexible gameplay systems (a data-driven scrapping/recipe system and an interruptible coroutine-based AI system) for the game Z-Poc, with a strong focus on architecture tradeoffs and performance. Shipped multiple iOS/Android educational games and improved a children’s book app experience by implementing lazy loading/background image loading to reduce initial load time; also built custom JSON-based networking for a turn-based business sim (JATitan).”
Nithya Nagaraj
Mid-Level Software Engineer specializing in cloud-native microservices and FinTech platforms
“Software developer with hands-on experience refactoring a legacy .NET CMS into a newer API-driven application (ASP.NET MVC, JavaScript/HTML), including dynamic asset migration and resolving team merge conflicts in Bitbucket. Built automated tests with PyTest and used Postman for API validation, and leveraged Splunk for production issue detection; also worked on an end-to-end ticketing/workflow management project with prioritization and verification steps.”
Hardik Arora
Mid-Level Full-Stack Software Engineer specializing in cloud-native web platforms and observability
“Built and shipped production LLM agents including an AI patient appointment assistant for Kyron Medical that automated specialist matching and end-to-end booking with email/SMS confirmations and a voice mode. Strong focus on production reliability (double-booking prevention with DB constraints and pre-write checks), deterministic multi-step orchestration with LangGraph, and rigorous monitoring/evaluation using LangSmith trace replay for prompt regression testing.”
Ashish Kapoor
Director of Digital Solutions & Principal Architect specializing in composable DXP and AI transformation
“Enterprise digital transformation technologist with SaaS and AI platform experience who positions themselves as a technical bridge for VC/startup contexts—focused on technical due diligence (scalability, technical debt, AI readiness) and helping portfolio companies build enterprise-grade, scalable platforms with AI-driven product development approaches.”
Shashwat Negi
Mid-level Software Engineer specializing in AI/ML and full-stack systems
“Data Scientist (2–3 years) at ZS Associates who has built and productionized agentic LLM systems, including a LangGraph-based multi-LLM prompt-optimization pipeline for entity extraction deployed as a Spring Boot microservice via Jenkins. Also built an Insightmate.ai chatbot and improved its RAG accuracy by diagnosing vector retrieval issues and implementing HyDE query expansion, while partnering with sales and pharma stakeholders to drive adoption (e.g., Zimmer Biomet platform migration into a multi-year partnership).”
Christian Babcock
Mid-level Software Engineer specializing in Unity simulations and gameplay systems
“Unity/C# gameplay engineer with strong multiplayer systems experience, including shipping a DarkRift-based server-authoritative architecture with prediction/reconciliation and measurable gains: frame time improved from ~30ms to 18ms and packet sizes dropped by roughly 40%. Also brings practical thinking around AI-assisted content tooling, using async service layers, JSON pipelines, and validation rules to support designers without disrupting production workflows.”
Ashutosh Singh
Mid-level Software Engineer specializing in backend systems and distributed platforms
“Built from scratch a social media analytics MVP featuring an LLM-powered semantic search agent that became a core part of the product experience within a 6-week deadline. Stands out for focusing on production readiness early—retrieval-first design, explicit tool constraints, structured outputs, idempotent services, and practical eval/monitoring loops rather than demo-only AI.”
Meet Vyas
Mid-level Software Engineer specializing in full-stack systems and AI applications
“Software engineer with 3 years at Gap Inc. who led a major modernization from monolith/legacy frontend to a React and Spring Boot microservices architecture, delivering 25% cost savings, 30% faster releases, and 50% performance gains. Also built a 0→1 startup product, MealShare AI, using managed cloud services to rapidly launch a real-time food redistribution platform.”
Chandana G
Mid-level Full-Stack Engineer specializing in cloud microservices and AI-powered platforms
“Full-stack engineer with hands-on experience building real-time operational products across banking, insurance, and startup e-commerce environments. They’ve owned features end-to-end—from React/TypeScript dashboards and Redux performance tuning to Spring Boot, Kafka, AWS Lambda, and production monitoring—and have also shipped 0→1 capabilities where business impact was immediate, such as reducing overselling through inventory visibility.”
SAI KIRAN Pagilla
Mid-level Full-Stack Developer specializing in .NET, Azure, and enterprise platforms
“Full-stack engineer with hands-on experience spanning React/TypeScript frontend architecture, .NET/Entity Framework backend services, and database optimization. Has owned end-to-end features like payment processing flows and also helped ship a 0→1 AI developer productivity capability, showing a mix of product sense, startup speed, and practical performance engineering.”
Jeremy Greenawalt
Principal AI Engineering Leader specializing in LLM integration and developer workflows
“Built and launched a production customer-service LLM agent/MCP system for a large website, enabling reps to search across millions of SKUs and manuals that were previously impractical to navigate manually. They emphasize maintainable AI architecture, staged rollouts, guard-agent evaluation, and multi-agent code review workflows spanning accessibility, performance, and security.”
Angela Churchwell
Principal Software Engineer specializing in enterprise AI platforms
“Built a production-grade LLM document processing and workflow orchestration platform at CBRE for internal operations teams, handling highly variable long-form documents with a reusable architecture involving 50+ coordinated LLM calls per request. Stands out for treating agentic systems like distributed backend infrastructure, with strong emphasis on evaluation, observability, reliability, and vendor-agnostic orchestration across Bedrock, Vertex AI, and OpenAI.”
Surya Ravula
Junior Software Engineer specializing in backend systems and AI automation
“Backend/platform engineer with Boston Scientific experience building secure healthcare integrations, resilient AWS data pipelines, and a production internal LLM support chatbot. Stands out for combining legacy-system modernization, strong reliability practices, and measurable operational impact in regulated healthcare environments.”
Srivatsava Vuppala
Junior Software Engineer specializing in AWS backend and Generative AI
“Engineer focused on AI-assisted software development and enterprise legacy modernization, with hands-on experience designing multi-agent workflows for code analysis, business logic extraction, BRD generation, and validation. Stands out for combining prompt design and agent orchestration with strong engineering discipline, including testing, CI/CD, and human review checkpoints.”
Arya Thorat
Mid-level Software Engineer specializing in cloud-native distributed systems
“Full-stack/backend engineer with deep experience building real-time fraud and credit-risk systems. Shipped an event-driven fraud monitoring platform (Kafka→MongoDB/Redis→WebSockets) delivering sub-200ms updates to 3000+ concurrent internal users, and built a Java/Spring Boot credit risk decisioning API that improved turnaround time by 30–40%. Strong AWS production operations (ECS Fargate/RDS/Redis) with proven incident response and performance tuning.”
Amit Verma
Mid-level Java Full-Stack Engineer specializing in healthcare and enterprise microservices
“Developer who actively integrates AI tools like Copilot, ChatGPT, and Cursor into day-to-day coding, testing, debugging, and framework learning to improve delivery speed. They also organize multi-agent workflows across code generation, review, testing, and documentation while retaining final ownership of quality and architecture decisions.”
Trishal Mudunuri
Mid-level Full-Stack Engineer specializing in healthcare platforms and cloud-native systems
“Built both a React/Supabase kanban product and CodeVoyage, a multi-agent platform for navigating large TypeScript/Node.js codebases. Stands out for being unusually rigorous about AI-assisted development: they quantify AI usage, manually verify generated code, and have firsthand experience debugging failures in persistence layers, retrieval quality, and long-context agent orchestration.”