Vetted Redis Professionals

Jaya Rathi

Screened

Senior Software Engineer specializing in distributed systems and cloud platforms

Ottawa, Canada15y exp

“Software professional with 13 years of experience across Canada and India, now seeking a first US role in California. Has practical experience applying LLMs and IDE agents like Cursor, ChatGPT, and Gemini to streamline engineering documentation workflows, especially automating Jira/Confluence process work to reduce manual effort and errors.”

Jessy Kattupalli

Screened

Mid-level Full-Stack Java Developer specializing in enterprise web applications

West Haven, CT4y exp

“Backend/full-stack engineer with hands-on experience building enterprise-scale real-time log analysis platforms using Spring Boot, Kafka, React, and observability tooling. Stands out for using AI tools heavily but responsibly—treating them as accelerators while relying on rigorous testing, architectural review, retry/DLQ patterns, and monitoring to ensure production reliability.”

Sharath Amula

Screened

Junior Software Engineer specializing in AI and FinTech

Dallas, TX2y exp

“Frontend engineer with experience in both healthcare and financial services, building high-stakes production interfaces such as AI-powered clinician care planning workflows and real-time fraud investigation dashboards. Stands out for combining React/TypeScript performance optimization with strong UX thinking in regulated, data-dense environments.”

“Built and iterated an end-to-end virtual waiting room for a real-time ticketing prototype, making concrete architecture tradeoffs (polling + Redis Pub/Sub) and improving performance post-launch with Redis caching (+30% throughput, -15% p99 latency). Also has hands-on experience building Spark/HDFS ETL pipelines with strong reliability/observability patterns and running disciplined NLP model evaluation loops on review-rating classification.”

“Built Taskline end-to-end as a solo founder: an AI-native operations platform for tradespeople with web and mobile apps, AI receptionists, invoicing, scheduling, and payments. Particularly interesting for teams seeking a zero-to-one full-stack builder who can turn LLM/agent capabilities into practical, low-friction products for non-technical users.”

“AI/full-stack product engineer with experience shipping agentic systems in both fintech and enterprise compliance contexts, including an Anthropic-powered compliance Q&A tool at American Express and a home-equity assessment experience for seniors. Stands out for combining strong product instincts, typed full-stack implementation, and rigorous LLM evaluation/monitoring practices to improve trust, adoption, and operational efficiency.”

“Full-stack/backend-leaning engineer with experience spanning fintech platforms, internal AI/RAG assistants, real-time analytics systems, and a zero-to-one academic web platform. Stands out for combining hands-on backend and infrastructure work with product ownership, team guidance, and measurable impact like cutting troubleshooting lookup time from 30 minutes to under 8 minutes and creating reusable UI components adopted across multiple projects.”

“Full-stack/product-minded engineer with strong distributed systems depth, spanning Spring Boot/Kafka microservices, Kubernetes observability, and large-scale React/TypeScript frontends. Particularly compelling for teams building real-time operational products: they describe owning payment/inventory services, designing telemetry dashboards for 150+ services, and helping move claims tracking from polling to event-driven architecture.”

“Built an agentic medical coding system at Optum that combined LangGraph, LangChain RAG, Azure OpenAI, pgvector, and TypeScript to automate routine clinical coding while escalating risky cases to humans. The system automated about 40% of routine cases at roughly 92% accuracy, with strong production evals and observability using MLflow, Ragas, and DeepEval.”

“Candidate brings a pragmatic, production-focused approach to AI-assisted software development, using AI as a pair programmer and conceptually applying multi-agent workflows across coding, testing, and review. They stand out for putting strong guardrails around AI usage—manual review, testing, SonarQube, peer review, and keeping critical logic manual—to improve speed without compromising security or code quality.”

“Worked at AMD on Vivado tool releases, focusing on adding and integrating new IP/SoC functionality into the Vivado flow and validating expected behavior through testing. Comfortable engaging with customers and open to travel for hands-on customer-facing work.”

“Backend/platform engineer from Dune Security with strong experience turning messy, fragmented workflows into reusable production systems. They’ve built a shared database abstraction layer, integrated multiple enterprise security platforms into a unified workflow, and shipped AWS Bedrock-powered security insight features with guardrails and human review.”

“Backend/AI engineer who has shipped an LLM-powered enterprise support-ticket agent at Comcast, building a production-grade microservices pipeline (FastAPI, SQS, Redis) with strong observability (OpenTelemetry/Splunk/Prometheus/Grafana) and reliability patterns (async, caching, circuit breakers, idempotency). Demonstrated quantified impact at scale—processing 10k+ tickets/day while improving response SLAs and routing accuracy through evaluation and human feedback loops.”

“Built and shipped an internal Financial Insights Assistant for banking analysts, owning the experience from workflow design through React frontend, FastAPI backend, and AI search integration. Particularly strong in making AI products usable and trustworthy by surfacing sources, experts, suggested prompts, and search history to improve confidence and speed.”

Mahima Mannava

Screened

Junior Software Engineer specializing in AI, LLM systems, and full-stack development

San Jose, CA3y exp

“AI/full-stack engineer who built a computer-usage agent end to end, including a split local/cloud architecture that used a vision LLM to drive real Chrome workflows while avoiding bot detection. Stands out for combining product-minded systems design, rigorous evals, and prompt iteration to achieve sub-2-second latency and a 20% reduction in automation failures in an early-stage environment.”

“Built and shipped an AI-powered support assistant at PNC Bank, owning both the React/TypeScript frontend and Python/FastAPI + LangChain/OpenAI backend. Stands out for applying RAG in a regulated banking environment with measurable impact: 34% better knowledge retrieval accuracy and 27% faster support lookup time.”

Jaya Rathi

Screened

Senior Software Engineer specializing in distributed systems and cloud platforms

Cupertino, CA15y exp

“Full-stack developer who built a unit billing subscription portal in React, Node.js, TypeScript, and PostgreSQL to track employee billable hours and generate paperless invoices, including BambooHR integration and async workflows with RabbitMQ. Also independently exploring agentic AI through a support desk assistant using RAG, ChromaDB, and LangChain, with a strong focus on grounding, type safety, and measurable retrieval quality.”

“Data/analytics-focused engineer who builds end-to-end KPI reporting and validation products used daily by plant leads and leadership to track yield, downtime, and defects. Combines Python/SQL + Power BI data pipelines with strong data-quality practices (automated validation, monitoring/alerts) and has experience designing scalable frontend architecture in TypeScript/React and working in distributed/microservices-style data systems.”

“Backend engineer with TaskRabbit experience building and operating payment/booking services in Python/Django on AWS (ECS + Lambda) with Kafka/SQS eventing. Demonstrates strong production reliability and incident ownership in high-stakes payment flows (idempotency, strict timeouts, retries, monitoring/alerting) plus data/ETL work in AWS Glue and measurable SQL performance wins.”

“Backend engineer who built an NL-to-SQL system at Target, using a multi-step LLM pipeline with vector-store schema retrieval and SQL validation to safely answer business questions. Strong in production FastAPI systems (async, Pydantic, Docker/Uvicorn, load balancing) and security (OAuth2/JWT, scopes, and database row-level security), with experience migrating Flask apps to FastAPI + PostgreSQL using strangler/feature-flagged canary rollouts.”

“Full-stack engineer who built an internal user-activity tracking and reporting system end-to-end using React/TypeScript, Node/Express, and Postgres, deployed on AWS (EC2/ALB, S3/CloudFront) with CloudWatch observability. Emphasizes reliability and data correctness via idempotent ingestion, retries with exponential backoff, backfills/reconciliation, and performance tuning as data scales, and has experience shipping quickly in ambiguous early-stage startup conditions.”

“Backend/data engineer with hands-on production experience across GCP and AWS: built FastAPI microservices on Cloud Run and delivered AWS Lambda + ECS Fargate systems with Terraform/GitHub Actions. Strong in data engineering (Glue/Spark, S3/Redshift) and modernization (SAS to Python/SQL), with proven reliability and incident ownership—including cutting a 20+ minute reporting query to under 2 minutes.”

“Backend/data engineer with production experience building secure Django/DRF APIs (JWT RS256 + rotating refresh tokens), background processing with Celery, and strong reliability practices (timeouts, retries/backoff, structured logging, audit trails). Has delivered AWS solutions spanning Lambda + ECS with IaC/CI-CD and built Glue/PySpark ETL pipelines with schema evolution and data-quality quarantine patterns; also modernized a legacy SAS pipeline to Python/PySpark with parallel-run parity validation and phased rollout.”

“Built and shipped a production multi-agent conversational AI platform (Monitor agent + RAG + 4 additional agents) with enterprise REST APIs, using ChromaDB-grounded WCAG knowledge to keep responses accurate while varying tone via personality modes and conversation memory. Has experience at LinkedIn delivering technical demos and pre-sales guidance to both engineering teams and C-level stakeholders, acting as a translator between sales and technical teams to drive adoption.”