Vetted Asynchronous Processing Professionals

“Built and productionized a multi-agent LLM analytics assistant at eBay that routes natural-language questions to retrieval or text-to-SQL, dynamically retrieves relevant schemas via a vector DB, and executes against a data warehouse. Drove a major quality lift (text-to-SQL accuracy 60%→85%) and materially reduced time engineers/PMs spent getting data insights through strong eval/monitoring, tracing, and reliability-focused design (schema retrieval, strict JSON outputs, retries/clarifications).”

“Backend/platform engineer with significant ownership at GoDaddy, where they built a real-time personalization and decisioning system that drove about $7M in annualized revenue and serves roughly 4M requests per day. Also operates as a solo engineer for a global human-rights legal-tech nonprofit, building the full platform and graph-based matching engine for 700+ partner organizations. Brings a strong blend of production backend rigor, platform thinking, and practical AI orchestration.”

“Backend/platform engineer with deep payments experience who built and operated a real-time transaction routing service end-to-end on AWS (Spring Boot, PostgreSQL/RDS, Redis, Kubernetes), delivering ~40% latency reduction and 99.99% uptime via strong resiliency and observability practices. Also productionized an internal LLM-powered RAG knowledge assistant with guardrails and a user-feedback-driven evaluation loop, and has led incremental monolith-to-microservices modernization using Strangler Fig and shadow traffic.”

Ruturaj Ghatage

Screened

Junior Software Engineer specializing in full-stack, cloud infrastructure, and applied AI

Herndon, Virginia2y exp

“Master’s student at UC San Diego who built an LLM-powered healthcare chatbot for patient history-taking and sepsis-related output, using a Node.js backend integrated with FastAPI for RAG/LLM interactions and a Flutter client. Also has healthcare AI startup experience deploying on AWS (ECS/Terraform/Docker) and implementing Kubernetes autoscaling to improve efficiency and reduce costs, with strong iterative evaluation in collaboration with a physician.”

Michael Matonte

Screened

Senior Backend Engineer specializing in distributed systems and AI-enabled platforms

Jersey City, NJ7y exp

“Backend engineer with end-to-end ownership experience in high-stakes environments spanning Citibank and industrial operations. They built an internal banking platform that automated complex entitlement workflows across thousands of business units with an 80% reduction in redundant processing, and they are now applying AI through OpenAI-powered agent workflows with RAG, vector databases, and security controls.”

Yash Jain

Screened

Director-level engineering leader specializing in identity, authentication, and AI security

Mountain View, CA12y exp

“Engineering leader at Intuit focused on identity, authentication, and security infrastructure for AI integrations in highly sensitive financial workflows. Led a 10-person Tier 1 team, built an AgentIdentity framework across 3 organizations, and helped launch TurboTax/QBO integrations with ChatGPT and Claude while maintaining strict reliability and security standards.”

Harsh Sanas

Screened

Intern Full-Stack Engineer specializing in AI and distributed systems

Los Angeles, CA2y exp

“Full-stack product engineer who has designed and shipped production web experiences in EV charging, trading, automotive companion apps, and AI systems. Stands out for owning user-facing React experiences through backend integration and production monitoring, with a strong bias toward reliability in real-time and high-stakes workflows. Also has early-stage Scale AI experience building a Text-to-SQL agent stack with Python, PostgreSQL, Redis, Kafka, and AWS.”

“Backend engineer who built and iterated a secure, multi-tenant RAG system over a large document corpus, emphasizing strict RBAC/ACL isolation, hybrid retrieval (vector+keyword), reranking, and strong observability to balance relevance, latency, and cost. Also led production refactors/migrations using strangler + feature flags/dual writes and has experience catching subtle real-world failure modes (including in a sensor calibration optimization pipeline).”

Manjory saran

Screened

Senior Backend & Infrastructure Engineer specializing in cloud-native distributed systems

5y exp

“LLM infrastructure engineer who built a production-critical real-time personalization and memory retrieval system for a user-facing product, adding <100ms P99 latency while improving relevance ~20–25% and holding SLA through 3x traffic. Experienced designing tiered retrieval backends (Redis + vector store), deploying on Kubernetes with autoscaling/circuit breakers, and running rigorous observability, incident response, and agent evaluation (shadow traffic, A/B tests, regression/replay).”

“Data engineer/backend developer with experience owning end-to-end, high-volume data pipelines for ML/analytics using Python, Airflow, SQL, and PySpark, reporting ~30% error reduction through improved reliability and data quality checks. Has also built Django-based REST APIs with caching/pagination and strong versioning practices, and operated external data collection/web scraping pipelines with anti-bot measures, monitoring, retries, and idempotent backfills.”

Matthew Frank

Screened

Senior Machine Learning Engineer specializing in computer vision and LLM-powered analytics

Santa Barbara, CA7y exp

“Machine learning engineer and startup veteran building InfraSketch (infrasketch.net), a full-stack system-design/diagramming product where users describe systems in plain English and an LLM agent generates and iterates on infrastructure graphs and exports design docs. Owns the entire stack (React/TS + FastAPI/Node, DynamoDB/Postgres, AWS serverless) and focuses on LLM consistency, modular agent architecture, and production-style CI/CD and reliability patterns.”

Vindhya Naidu

Screened

Mid-level Backend Software Engineer specializing in Java/Spring microservices and FinTech

Austin, TX5y exp

“Backend engineer with Apple experience who owned production platform improvements end-to-end, including a Redis caching layer that cut API latency ~35–40% and reduced DB load. Has hands-on on-call/incident response and observability (CloudWatch), plus experience scaling Docker/Kubernetes microservices and operating Kafka-based telemetry pipelines with schema evolution, deduplication, and replay/backfill handling.”