Vetted Redis Professionals

Andrew Park

Screened

Mid-Level Software Engineer specializing in QA automation and full-stack web development

Los Angeles, CA7y exp

“Automation and customer-facing technical specialist with experience at QA Wolf and Kalmar, taking Playwright E2E testing from prototype scripts into CI-gated production pipelines (80%+ automated coverage). Also trained both software teams and industrial operations audiences (Fleet View for automated straddle carriers) and partnered with sales/customer teams to translate technical results (e.g., Siemens PLC updates, QA ROI) into adoption and faster closes.”

Pravelliika Chirivella

Screened

Mid-level Full-Stack Developer specializing in Django/React and FinTech automation

New York, NY3y exp

“Product-minded full-stack engineer who owns customer-facing products end-to-end and iterates quickly using MVP validation, feature flags, automated testing, and staged rollouts. Has built TypeScript/React systems with modular backends and designed microservices with async messaging (RabbitMQ), handling scale issues like ordering, retries, and idempotency. Also delivered an internal ops automation tool with a self-serve real-time workflow dashboard that reduced errors and drove rapid adoption.”

Tejas Belakavadi Kemparaju

Screened

Mid-Level Software Engineer specializing in backend, microservices, and ML systems

Newark, NJ3y exp

“Primary designer/implementer/maintainer of an open-source JavaScript library for programmatic SSML generation and validation in text-to-speech pipelines. Focused on safety-by-default APIs with vendor-specific extension adapters, strong backward compatibility/deprecation practices, and measurable performance gains by removing redundant validation stages. Emphasizes developer experience through example-driven documentation and systematic community issue triage.”

Naisarg Halvadiya

Screened

Senior Full-Stack & AI Engineer specializing in scalable web platforms and LLM automation

Bengaluru, India4y exp

“Built a production agentic AI assistant in Python using Playwright plus Google Gemini’s vision capabilities to automatically document and execute UI workflows step-by-step, reducing developer time spent on trivial documentation/knowledge transfer. Also built an Apache Airflow ETL pipeline and has experience evaluating AI agents with human-in-the-loop methods, plus successfully communicated a vision-model-based CMS analytics PoC to non-technical university stakeholders and proposed it to Academic Technology with cost-savings rationale.”

Yang Qi

Screened

Mid-level Full-Stack Software Engineer specializing in AI platforms and data visualization

Cambridge, MA3y exp

“Full-stack engineer with healthcare/bioinformatics experience who built a real-time genomic data analysis and 2D visualization feature (React/TypeScript + D3, FastAPI) at University of Utah Health, deploying on AWS ECS Fargate with monitoring and measuring engagement via Google Analytics. Also built AWS Lambda-based ETL pipelines for lab data ingestion using pandas/NumPy with reliability patterns (idempotency, retries, CloudWatch alerting) and drove maintainability improvements through shared component libraries and React hooks.”

“Hands-on backend engineer with practical experience improving latency in Django-based API systems by fixing missing indexes and eliminating N+1 queries. Also built an AI scheduling system using FastAPI, a relational database, AI/ML workflows, and an operational reporting dashboard, with a clear bias toward correctness and maintainable architecture.”

Sai Prudhivi

Screened

Mid Software Engineer specializing in full-stack microservices and cloud platforms

New York, NY4y exp

“Backend-focused engineer with experience building high-volume policy management and enterprise pricing systems, including Django/FastAPI/Flask services, Kafka-based async workflows, and Prometheus/Grafana observability. While they have not yet shipped a customer-facing AI agent or production LLM integration, they bring strong cloud, API, reliability, and scalable system design fundamentals that translate well to responsible AI infrastructure work.”

“Backend/platform engineer with strong ownership of production systems, including a full Azure migration from a VM-based monolith to a containerized, event-driven microservices architecture. They combine cloud infrastructure, LLM/RAG optimization, and pragmatic stakeholder management, with measurable wins including 90% infra cost reduction, faster deployments, and significantly improved latency and token efficiency.”

“New grad candidate with graduate research experience building a multi-agent RAG pipeline from scratch, including worker-coach orchestration and an evaluation framework. Most notably, they improved structural similarity from 67% to 98% by designing validation and retry logic to reduce hallucinations, showing strong practical depth in agentic AI systems.”

Sasi Kanduri

Screened

Senior Full-Stack Software Engineer specializing in backend systems and workflow platforms

Sacramento, CA7y exp

“Full-stack engineer with strong React and Python backend depth who has owned complex analytical products end-to-end, from performant UIs to FastAPI services, SQLAlchemy data models, Redis caching, and production observability. Particularly compelling is their 0→1 automation work in the water systems domain, where they built Airflow- and LLM-powered workflows that reduced manual notification and correction work by 90%.”

Shrey Patel

Screened

Junior Software Engineer specializing in AI-driven backend and full-stack systems

New Brunswick, NJ2y exp

“Full-stack AI engineer who has built both a healthcare voice-feedback system for Rutgers Health and an LLM-powered meme generation pipeline at Attention.ad. Stands out for combining React/TypeScript, FastAPI, Postgres, real-time systems, and frontier-model orchestration with practical product instincts, including measurable latency/cost improvements and strong iteration based on user feedback.”

“AI engineer focused on production-grade LLM systems rather than prompt-only solutions, with hands-on experience building citation-grounded RAG products and multi-agent workflows. Most notably built a financial document intelligence system for SEC filings and contracts that achieved ~92% recall@5, cut latency below 2 seconds, reduced hallucinations, and turned analyst research from hours into seconds.”

“Engineering leader with hands-on architecture depth who has improved team delivery cadence to weekly releases by redesigning SDLC ownership and validation workflows. Currently leading AI/LLM product work around meeting-transcript intelligence for sales use cases, with a strong emphasis on observability, prompt tuning, and iterative UX refinement.”

“Senior backend/full-stack engineer with strong e-commerce and marketplace experience in scaling-stage startup environments. Stands out for redesigning a high-traffic inventory reservation system with Redis and PostgreSQL to achieve zero oversell incidents, while also contributing across Go, Python, and React/Next.js production systems and vendor-platform integrations.”

“Full-stack software engineer who shipped production systems in academic and e-commerce contexts, including a UC Irvine course recommendation platform with async Kafka-based OCR processing (Tesseract) and LangChain-driven recommendations. Strong in building polished React/TypeScript dashboards (Figma-to-implementation) and owning Python backends (FastAPI/Flask) with solid API design, caching, and AWS EKS deployments; delivered measurable impact (tripled engagement, ~50% faster processing).”

“Backend/data engineer with strong production experience across Python microservices (FastAPI) and AWS serverless/data platforms (Lambda, API Gateway, Glue, Redshift). Demonstrates reliability and incident ownership (rate limits, retries/circuit breakers, monitoring) and has delivered measurable SQL performance gains (12–15s to <800ms, ~60% CPU reduction). Seeking fully remote work and not open to relocation/onsite meetings.”

“Built and shipped a production GPT-4 + RAG customer support chatbot that materially improved support operations (response time 4 hours to <3 minutes; ~65% tier-1 ticket automation). Demonstrates strong end-to-end LLM engineering across retrieval (Sentence Transformers/Pinecone), safety (multi-layer moderation), cost/latency optimization (caching/streaming, Celery/Redis), and rigorous evaluation/monitoring (shadow deploys, Datadog, 500+ test cases), plus proven stakeholder buy-in leading to 80% adoption.”

“Built and deployed a scalable, production-ready LLM knowledge assistant using a RAG architecture (LangChain + vector store/FAISS) to replace keyword search for internal documents. Demonstrates hands-on expertise in hallucination reduction and retrieval quality improvements through semantic chunking, similarity tuning, prompt design, and human-in-the-loop validation, plus strong stakeholder communication via demos and visual explanations.”

“Engineer with experience in regulated healthcare and financial systems, including a United Health healthcare service migration to AWS. Built documentation-as-code for CI/CD (Jenkins/Docker/Kubernetes/Terraform + GitHub Actions) that accelerated release cycles from 3 weeks to 4 days and tied security configuration (Spring Security/OAuth2/JWT) directly to HIPAA/GDPR compliance. Strong in observability-led incident response (ELK/Prometheus/Grafana) and performance tuning (PostgreSQL, async processing), citing MTTR reduction from 3 hours to 50 minutes and support for 250K+ concurrent users.”

“Master’s program project lead who built and deployed a real-time sound recognition system (Flask + React Native + ML) that was adopted by 200+ university students. Demonstrates strong production engineering and cross-layer debugging—solving latency, unreliable uploads, and observability gaps using microservice separation, chunked/idempotent transfers, and packet-capture-driven network diagnosis—plus AWS/on-prem and IoT edge-to-cloud integration experience.”

“Full-stack engineer focused on data-heavy platforms, building Spring Boot microservices and Angular/React dashboards end-to-end. Has hands-on experience improving large-scale API and UI performance (including cutting 8–10s response times) and ensuring cross-service consistency using Kafka, idempotent consumers, and strong validation/transaction patterns on AWS with CI/CD and observability (Prometheus/ELK).”

“Backend-focused engineer who owned an AI mentoring workflow platform built in Django with LangGraph multi-agent orchestration, optimizing it to stay under 200ms latency while scaling past 1,200 active users using profiling, caching, load testing, and OpenTelemetry-style tracing. Also has hands-on experience containerizing and deploying Python/ML services to AWS ECS via GitHub Actions/GitOps, and building reliable real-time pipelines with webhooks and Redis queues (idempotency, backpressure, DLQ).”

“Full-stack AI engineer who built Skillmatch AI, an LLM/RAG-based job matching platform using FastAPI microservices, Airflow-orchestrated async pipelines, and Pinecone vector search (sub-second retrieval across 50k+ vectors) deployed on GCP with autoscaling. Also partnered directly with a cancer researcher to automate SEER + PubMed-driven report generation via an AI pipeline, emphasizing rapid prototyping and outcome-focused communication.”