Vetted Redis 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%.”

“Full-stack builder with hands-on experience shipping both location-based consumer products and AI-driven data platforms. Has owned end-to-end systems across React/Next.js, FastAPI, PostgreSQL, Streamlit, and geospatial tooling, with a strong emphasis on modular architecture, LLM reliability, and turning messy real-world data into usable product experiences.”

“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.”

“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.”

“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.”

Ryan Crabbe

Screened

Junior Full-Stack/Mobile Engineer specializing in React Native and NestJS

Remote1y exp

“Built an AI-powered restaurant menu rewriting app that generates diet-constrained menus from photos, with a backend designed around bounded contexts and a lightweight CQRS approach. Demonstrates strong multi-tenant PostgreSQL design (RLS, tenant-scoped queries) and performance tuning (partitioning, keyset pagination, composite/partial indexes), plus AI workflow orchestration using Redis/BullMQ and Vercel AI SDK with structured outputs and evals; reduced p95 latency ~35–50% via racing LLM requests and caching.”

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.”

“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 a production AI-powered workflow automation system at Netflix that integrated OpenAI and LangChain with FastAPI services on AWS, cutting roughly 320 hours of manual operational effort. Brings a mix of full-stack product development and practical AI systems experience, with strong attention to reliability, maintainability, and non-technical user adoption.”

“Robotics software engineer from Ati Motors who brought a Linear MPC approach (based on Kuhne et al.) into production, rebuilding parts of the planning stack to eliminate oscillations and safely double AMR speed from 0.8 m/s to 1.6 m/s. Also delivered an end-to-end point-cloud detection pipeline (PointPillars) including synthetic data generation in Isaac Sim and TensorRT deployment for real-time human/trolley detection, with a strong focus on production reliability via iterative hardening and nightly SIL.”

“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.”

Zheng Wu

Screened

Junior Software Engineer specializing in backend systems and cloud messaging

Mountain View, CA1y exp

“Data/ML engineer who has owned end-to-end systems across email deliverability/segmentation and production LLM apps. Built a Spark+Airflow segmentation engine that materially improved deliverability (99.9%) and open rates (>50%), and shipped a PDF-to-quiz RAG product using LangChain/Vertex AI/Chroma with strong guardrails and an eval loop that cut hallucinations to <5%.”

Asrith Velireddy

Screened

Mid-level AI/ML Engineer specializing in MLOps, LLMs, and scalable ML systems

Harrison, NJ4y exp

“ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.”

Sri Charan Reddy Mallu

Screened

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

Redwood City, CA5y exp

“Full-stack engineer with experience across Magna, C3.ai, and Amazon, building GenAI-enabled products and finance transaction systems. Has shipped Next.js (App Router) + TypeScript features backed by Go/Python RAG pipelines, and emphasizes production quality via load testing, Selenium regression coverage, LLM-aware integration testing, and Azure observability. Also built LangGraph-orchestrated multi-step content generation workflows with robust retry/idempotency strategies.”

Jingyao Chen

Screened

Junior Backend/Platform Engineer specializing in AI microservices and cloud-native systems

Pittsburgh, PA2y exp

“Cofounder at MeowyAI who shipped a production multimodal (vision/voice/text) AI task manager using Gemini, tackling real-world issues like hallucinations, tool-calling safety, and RAG-based preference memory. Also built a production multi-agent RAG system orchestrated with LangGraph (and contributes to LangChain), with strong emphasis on latency optimization, observability (OpenTelemetry), and rigorous testing/evaluation including A/B tests and adversarial prompting.”

KARTHIKBABU VADLOORI

Screened

Mid-level Full-Stack Developer specializing in Spring Boot, React, and cloud microservices

San Francisco, CA5y exp

“Backend engineer with experience at Meta and Accenture building regulated-data systems (healthcare/financial) using Python/Flask and Postgres. Has scaled high-throughput services to millions of daily requests, delivering measurable latency wins (~40% API latency reduction; ~35% faster DB-backed endpoints), and has productionized ML inference services using Docker/Kubernetes and AWS (ECS/SageMaker).”

Meredith Ma

Screened

Entry-level AI/ML Software Engineer specializing in generative AI and computer vision

Pittsburgh, PA2y exp

“Built and owned a production RAG coding assistant at Magna International used by 200 engineers, with hands-on work across React/TypeScript, retrieval infrastructure, and Postgres observability. Also brings an unusual blend of product UX thinking from AR game onboarding work, showing strength in both technical systems reliability and user activation.”

“Technical lead who shipped a production GPT-4-powered customer support agent for Square, serving a large fintech customer base through a React chat interface with tool-using orchestration, guardrails, and live handoff paths. Brings strong real-world experience in agent reliability, evaluation, observability, and workflow orchestration using Temporal, Sidekiq, Pinecone, Datadog, and Snowflake.”

“Software Developer at Amazon Robotics who co-developed a congestion-aware path planning system optimizing robot routes across 23 warehouses. Built and operated a real-time, service-integrated pipeline using AWS (AppConfig, DynamoDB), Java, and Redis caching, and has hands-on experience debugging robot behavior on-site with rigorous testing and staged releases.”

“Internship experience deploying cloud-based services into production, including navigating security/resource provisioning and coordinating approvals across impacted teams. Built a Python backend for a local Ollama-based app using open-source models, and has hands-on distributed systems experience implementing and debugging Paxos with extensive logging/state tracing.”