Vetted Redis Professionals

“AIX/IBM Power Systems engineer with hands-on production incident leadership in a regulated banking environment, using deep OS-level tooling to diagnose CPU entitlement and memory pressure issues. Experienced with HMC/vHMC, VIOS, and zero-downtime DLPAR resizing, plus PowerHA/HACMP clustering and validated failover testing. Also drives migration readiness via Bash/Python automation (60% manual-effort reduction) and phased AIX cloud/hybrid cutovers.”

“Robotics software engineer with autonomous driving lab experience at UCSD, building and optimizing ROS2 perception and control pipelines (camera-based real-time object detection) with a strong focus on low-latency performance and robust message interfaces. Also brings production deployment experience from Hewlett Packard Enterprise, using Docker and Kubernetes for containerized environments and deployment pipelines.”

“Backend/founding-engineer-style builder who designed and evolved a near-real-time customer churn prediction platform (FastAPI + AWS SageMaker/Lambda + Redis + MLflow) to enable real-time retention actions, reporting ~18% churn reduction. Demonstrates strong production engineering in secure API design, incremental migrations with data integrity safeguards, and robustness improvements in async pipelines (idempotency, DLQs, retry visibility).”

“Backend/data engineer with strong AWS production experience across containers (ECS) and serverless (API Gateway/Lambda/SQS), plus Glue-based ETL to Parquet for Athena/Redshift. Demonstrates hands-on reliability and security depth (Cognito OAuth2/JWT with JWKS rotation, idempotency/DLQs, monitoring) and measurable performance wins (Redis caching + query tuning), along with legacy-to-services modernization using parallel-run parity and feature-flagged cutovers.”

“Backend engineer with hands-on experience scaling a Python/Flask incident-logging platform processing thousands of daily logs. Strong in performance tuning (PostgreSQL/SQLAlchemy query optimization, partitioning, summary tables) and reliability patterns (Redis caching, Celery background workers, Docker + Jenkins CI/CD), with some multi-tenant data isolation experience via separate DBs/schemas.”

“Built and productionized an LLM-based PDF extraction pipeline for Medicaid policy documents by fine-tuning Gemini Flash 2.0 and deploying via Vertex AI, adding validation/guardrails to improve trust and reliability. Also built and scaled a SaaS platform (cnotes) for cable operators and regularly partners with customers and sales teams through interactive demos, rapid iteration, and real-time workflow debugging.”

“GenAI/ML engineer from Lucid Motors who built and productionized an LLM-powered RAG diagnostic assistant for manufacturing and maintenance teams, deployed on AWS with Docker/Kubernetes and MLflow. Demonstrates end-to-end ownership from retrieval/prompt design to scalability, monitoring, and workflow integration via APIs, plus production ML pipeline orchestration with Kubeflow (Spark/Kafka + TensorFlow) for predictive maintenance use cases.”

“Backend engineer at Goldman Sachs who deployed internal LLM-powered utilities to summarize operational logs/tickets, with a strong emphasis on data sensitivity and reliability. Built deterministic workflows with template-based prompts, confidence checks, and rule-based fallbacks, and used monitoring plus failure-rate metrics to tune performance; also has hands-on Temporal orchestration experience for resilient async backend jobs.”

“Built and deployed a production LLM-powered RAG assistant for semiconductor manufacturing failure analysis, reducing engineer triage effort by grounding outputs in retrieved evidence and gating responses with SPC + ML signals (LSTM anomaly scores, XGBoost probabilities). Experienced with LangChain/LangGraph to ship reliable, observable multi-step agents with branching/fallback logic, and evaluates impact using both technical metrics and business KPIs like mean time to triage and downtime reduction.”

“Early-career technologist with internship experience deploying and fixing production/demo service configurations despite poor documentation, using step-through debugging and prior demo references to restore progress quickly. Also has hands-on IT technician experience diagnosing hardware failures (hard drive damage), recovering files, and replacing drives, plus QA test development in Python for customer-reported bugs and a strong focus on networking (monitoring, IP activity analysis).”

“Software engineer with hands-on experience improving performance and reliability in financial workflows (settlements/loan processing), spanning React/TypeScript and Angular frontends plus Spring Boot microservices. Has delivered measurable latency improvements using PostgreSQL optimization and Redis caching, and has operated Kafka-based systems at scale with idempotent processing and backoff/retry strategies while iterating internal ops tooling with support/finance teams.”

“Software engineer/product-focused builder who has delivered customer-facing dashboards (React/TypeScript + Spring Boot) and microservices using RabbitMQ, emphasizing safe, fast iteration with CI/CD, feature flags, and monitoring. Also built an internal monitoring/reporting tool adopted by ops/support by involving users early and iterating based on feedback.”

“Software engineer working on an e-commerce platform, currently building a RAG-based recommendation system with a team new to the technology. Has delivered an end-to-end React/TypeScript website for a local car dealer and built an internal "encryption as a service" tool to secure sensitive data across repositories and through release/UAT, with experience debugging microservices integration issues.”

“Built and owned major parts of a real-time distributed AI fraud-detection pipeline (ingestion, inference microservice integration, and automated action layer), optimizing latency and observability and reducing false positives by ~35%. Understands ROS/ROS2 concepts (nodes/topics/services) and planned hands-on ramp-up via ROS2 pub/sub exercises and Gazebo simulation, but has not worked on physical robots or ROS in production.”

“Software engineer with experience building a Vue.js/TypeScript internal component library (with Jest testing standards) and improving JS runtime performance via profiling, code splitting, and lazy loading. Also led documentation and community support for a Python ML utility library, diagnosing metric-calculation bugs for imbalanced datasets and driving large reductions in support inquiries through targeted docs, tests, and rapid hotfixes in a startup environment.”

“Red Hat ML/LLM engineer who designed and deployed a production LLM-powered customer support automation system using RAG, improving latency by 30% via PEFT and vector search optimization. Built security and governance into retrieval (access-level filtering, encrypted Pinecone/ChromaDB) and delivered SHAP-based explainability via a dashboard for non-technical stakeholders. Experienced orchestrating distributed ML/RAG pipelines across AWS SageMaker and OpenShift with Airflow/Prefect, plus multi-agent workflows using CrewAI and LangGraph.”

“Platform-focused engineer experienced in productionizing ML/LLM systems: containerized a local prototype, implemented CI/CD, deployed to Kubernetes with scaling controls, and added monitoring/logging. Comfortable diagnosing real-time issues in LLM/agent workflows using logs/metrics and incident stabilization tactics, and supports sales calls by clearly explaining production scalability to unblock customer decisions.”

“LLM/RAG engineer who has shipped production systems in high-stakes domains (fraud analytics at Mastercard and security compliance as a CI/CD gate). Strong focus on reliability: hybrid retrieval for latency, citation-backed outputs for trust, and code-driven eval/regression pipelines using golden datasets. Also built scalable OCR-based ingestion for messy classroom artifacts (handwriting, PDFs, whiteboard photos) using Go/Python and cloud services.”

“Full-stack engineer with e-commerce experience who shipped and owned an order history dashboard using Next.js App Router/TypeScript, combining server components for SEO/perf with client-side interactivity via React Query. Has backend reliability experience (Temporal order-processing workflows, Postgres modeling/indexing, and payment API idempotency keys), and emphasizes production stability, observability, and zero-incident launches.”

“Data engineer with experience delivering an end-to-end pipeline handling ~3.5TB in a star-schema setup (fact + dimensions) and producing business-facing tables in Hive/Spark. Identified and resolved UAT-reported duplicate issues caused by joins through root-cause analysis, and also built automation to run Spark SQL metrics on weekly/monthly/quarterly cadences and distribute results to users.”

“Data engineer focused on fraud and banking analytics who has owned end-to-end batch + streaming pipelines at very large scale (hundreds of millions of records/day). Built robust data quality/observability layers (schema validation, anomaly detection, alerting) and delivered low-latency serving via AWS Lambda/API Gateway with DynamoDB + Redis, plus external data ingestion/scraping pipelines orchestrated in Airflow with anti-bot protections.”

Mahesh Kumar Duvvuri

Screened

Senior Full-Stack Software Engineer specializing in microservices and cloud-native systems

New York City, NY4y exp

“Backend/infra engineer with experience across Nestle, J.P. Morgan, and Capgemini, combining ML systems work (YOLOv8/PyTorch object detection with TFLite edge deployment) with production-grade cloud/Kubernetes operations. Has delivered measurable impact via AWS migrations (25% cost reduction, 99.9% availability), microservice modernization (35% faster processing), and low-latency Kafka streaming for financial dashboards (<100ms) using DLQs and idempotent consumers.”

Abhinav Gupta

Screened

Junior Machine Learning Engineer specializing in LLMs and applied data science

2y exp

“Built and shipped multiple production AI systems, including Auto DocGen (LLM-generated OpenAPI docs kept in sync via AST diffs, schema-constrained generation, and CI/CD on Render) and a multimodal sign-language recognition pipeline at USC orchestrated with FastAPI, MediaPipe, and PyTorch. Also partnered with Esri’s non-technical community team to fine-tune an LLaMA-based spam classifier with a review UI, cutting moderation time by 70%.”