Vetted Java Professionals

“Candidate has developed a disciplined AI-first engineering workflow that combines design docs, prior PR analysis, testing plans, and multi-agent coordination to accelerate delivery without sacrificing quality. They described acting as a tech lead for AI agents, overseeing code structure, business logic, testing, and service contracts, and reported reducing manual coding effort by nearly 80%.”

Ashraf Jahangeer

Screened

Executive engineering leader specializing in AI, SaaS, and large-scale platform transformation

Pleasanton, CA19y exp

“Senior engineering executive with 20+ years of experience scaling and restructuring product engineering organizations across startups and larger companies. Over the last 8 years, they grew teams significantly at Stitch Fix and EasyPost, led monolith-to-microservices modernization, and launched products associated with 50M-100M+ ARR. Currently a hands-on VP Engineering at BlueCargo, combining executive leadership with direct technical contribution when customer-critical issues arise.”

“Full-stack engineer with a strong mix of real-time product engineering and applied AI experience. Built and deployed a production stock trading simulator on AWS and an LLM-based customer support agent with RAG/tooling, and also shipped a zero-to-one in-store detection feature at Meituan that improved CTR by 7% and conversion by 11%.”

“Backend/data engineer with production experience building FastAPI services with strong reliability patterns (circuit breaker, rate limiting, caching, graceful degradation) and JWT/OAuth2 auth. Has delivered AWS EKS deployments via Terraform with Secrets Manager/IRSA and HPA autoscaling, and built Glue/Spark ETL pipelines on S3 Parquet with schema-evolution and idempotent reruns; also demonstrated measurable SQL tuning impact (20–30s to <10s).”

“Backend/distributed-systems engineer with Uber experience building real-time telemetry and safety signal pipelines. Strong in Kafka-based event-driven architectures, low-latency processing under peak load, and production reliability via monitoring, retries, and fallback logic; has Docker/Kubernetes and CI/CD deployment experience.”

“Full-stack engineer with deep serverless AWS experience who built VidToNote, an AI video analysis platform, end-to-end using Next.js App Router/TypeScript and an event-driven pipeline (API Gateway, Lambda, DynamoDB, S3, Step Functions, SQS). Strong on production reliability and observability (CloudWatch, X-Ray, structured logging), plus data/analytics work in Postgres with measurable query optimizations and durable LLM evaluation workflows. Amazon background; integrated 22 AWS services and completed AWS Solutions Architect Professional certification within a month.”

“Full-stack engineer in the investment tracking/financial reporting space who built an automated reporting dashboard and compliance/reporting pipeline end-to-end using Next.js (App Router, server/client components), REST, and Postgres. Demonstrated measurable performance wins (~30% faster loads) through caching and query optimization, and built durable orchestrated workflows in n8n with retries, idempotency, and reconciliation checks.”

“Amazon engineer who built an Amazon Bedrock-powered summarization layer over large-scale network/service telemetry (“top talker” insights) to help security engineers triage anomalies faster. Emphasizes production-grade design patterns for LLM features—non-blocking enrichment, deterministic fallbacks, strict structured outputs, and monitoring to preserve trust in source-of-truth telemetry.”

Andrew Liang

Screened

Intern Software Engineer specializing in full-stack and AI/ML systems

2y exp

“Software engineer with experience at Amazon and Agora building end-to-end systems: a knowledge-base AI chatbot (React/TypeScript UI + retrieval/response backend + Docker deployment) and an internal approval governance platform using AWS Step Functions and DynamoDB. Emphasizes fast iteration without sacrificing trust via feature-flag rollouts, citation-required answers, abstention on low-confidence retrieval, regression query sets, and strong observability (request IDs, structured logs, latency/error monitoring).”

“Early-career engineer (internship background) who built a production-style notes product using Next.js App Router with Server Components/Server Actions and a Postgres-backed analytics model. Demonstrates strong performance and reliability instincts—measured DB latency improvements via indexing and cursor pagination, plus durable orchestration with Temporal using idempotency and deterministic workflows.”

Yeshwanth Sai Pala

Screened

Mid-level Full-Stack Developer specializing in cloud microservices and AI-driven FinTech

Remote, USA4y exp

“Stripe engineer who shipped an end-to-end merchant fraud insights dashboard, spanning Spring Boot/Kafka risk-scoring services and a React+TypeScript UI. Focused on low-latency, high-volume transaction processing and production operations on AWS (EKS/CloudWatch), including handling a real traffic-spike latency incident via query optimization, indexing, and rate limiting.”

Param Yanamandra

Screened

Staff/Lead Software Architect specializing in Contact Center platforms and GenAI automation

Campbell, CA21y exp

“Built and deployed production LLM systems in healthcare and at LinkedIn: automated pen-and-paper clinical trial evaluations with a 40x efficiency gain and created an evidence-based Evaluation Agent focused on accuracy and speed. Also used Temporal to orchestrate resilient data-ingestion workflows for customer support staffing prediction, improving prediction outcomes by 40% while handling missing data, retries, and backfills.”

Vela Sivasankaran

Screened

Executive technology leader specializing in cloud, telecom, and digital transformation

Virginia, USA16y exp

“Former founder of a financial services technology startup that is currently on hold for family reasons. Has hands-on startup fundraising exposure from employee roles, including presenting proof-of-concept demos to venture capital firms, and brings a strong focus on fraud prevention, safeguards, and regulatory compliance.”

“Built and shipped a production agentic RAG system for healthcare analysts that automated compliance/operations knowledge retrieval across PDFs, reports, and databases. Emphasizes production reliability (monitoring, retries, fallbacks, async queues), strong evaluation/iteration loops, and measurable impact (3–10s responses and ~98% top-k retrieval accuracy).”

“ML/AI engineer with experience at Uber and Scale AI, focused on customer service automation across both classical NLP and generative AI systems. Has owned systems from experimentation through production on AWS, including LLM fine-tuning, RAG optimization, safety evaluation, and internal Python platform tooling that improved consistency and engineering velocity.”

“Built and shipped a production internal framework-adoption agent for design system leadership, using Temporal, Google ADK, and a Slack bot interface. They appear to be an early internal builder of agentic systems at their company, with practical experience in prompt/process design, lightweight orchestration, and reliability tradeoffs for real-world LLM workflows.”

“Meta engineer who emphasizes AI-native development workflows, using Claude Code heavily to ship UI and performance fixes quickly. Notable examples include a location-aware ad relevance feature that increased CTR and revenue, and a vehicle insights chatbot whose UX improved through metric-driven prompt tuning.”

“Internship experience at Intuit building an LLM-grounded QA system for internal microservice data across 100+ microservices, using a graph database approach (evaluated Neo4j and selected AWS Neptune for production alignment). Also has UC Berkeley research experience (including work with Prof. Dawn Song / Berkeley Eye Research Lab) and cross-functional collaboration with bioinformatics/biology teams to deploy software systems on research servers.”

“Backend owner at Apple for an AI workflow orchestration service, with hands-on experience stabilizing peak-traffic production systems using OpenTelemetry-style tracing, bounded async concurrency, and database performance tuning. Built and shipped a Python LLM-agent orchestration layer to automate multi-step operational workflows, emphasizing guardrails, auditability, and deterministic fallbacks to keep non-deterministic AI behavior production-safe.”

“Gameplay engineer with hands-on ownership of a real-time C++ combat ability system, including diagnosing and eliminating large-scale combat frame spikes by refactoring hit detection to an event-driven, animation-notify approach (cut collision checks ~80%). Also implemented UE5 networked abilities (dash) with client-side prediction and server-authoritative reconciliation, plus projectile ballistics validated through debug spline visualizations and unit tests.”

“Meta engineer focused on reliability/operations tooling who built a unified real-time health dashboard and scalable telemetry pipelines (AWS + Datadog) for thousands of devices. Also shipped an internal LLM-powered knowledge assistant using RAG over wikis/runbooks/logs with strong guardrails and a rigorous eval loop that drove measurable accuracy improvements via automated doc ingestion and embedding updates.”

“Series C (Sigma Computing) full-stack engineer/intern who shipped production features across React/TypeScript and GraphQL, including a Recents/workbook activity reliability improvement that handled unsaved “exploration” events via deterministic backend updates. Emphasizes production quality through Jest/Cypress coverage and feature-flagged staged rollouts, and is recognized for UX-focused improvements (fast, accurate filtering at scale) and proactive cross-functional ownership.”

Derek Tuggle

Screened

Executive Robotics & Machine Learning Engineer specializing in industrial IoT controls

San Francisco, CA6y exp

“VP of New Product Development at Axiom Cloud who built and scaled a "Virtual Battery" product that used supermarket frozen inventory as thermal energy storage—personally prototyped core control/safety logic in Python and led the engineering buildout through deployment and operations. Combines real-world industrial controls and edge deployment experience (LonWorks/Modbus, Docker/CI/CD) with an MS in CS focused on robotics, perception, and ML, including ROS 2 and YOLO-based perception.”

Dheeraj Kumar

Screened

Intern Data Scientist specializing in marketing analytics and data engineering

Tucson, Arizona2y exp

“AI/LLM practitioner with internships at Dell Technologies and Roche who built and deployed a healthcare-focused "Doctor LLM" by fine-tuning Meta Llama 3.2 on healthcaremagic.json, emphasizing safety guardrails to prevent harmful medical advice. Experienced in productionizing AI workflows with monitoring, testing, and orchestration (Airflow, Kubernetes), and in delivering AI-agent-driven competitive landscape insights to non-technical business stakeholders.”