Vetted Caching Professionals

Saisureshreddy Challa

Screened

Mid-level Data Scientist specializing in AI/ML, LLMs, and domain analytics

California, USA6y exp

“BlackRock AI/ML engineer who built and owned a production LLM document intelligence system for regulatory and investment analysis end-to-end. They combined RAG, multi-agent validation, strong evaluation/monitoring, and reusable Python services to process 50K+ documents, cut review time 40-50%, and improve decision accuracy by about 25%.”

“ML/AI engineer with hands-on ownership of production computer vision, speech, and legal RAG systems. Notably improved a key-duplication CV pipeline enough to unblock commercial launch and remove specialist manual measurement, and also shipped a live Quran recitation detection feature for a product with 1M+ users.”

“Built a production AI-powered insights platform for marketing teams analyzing large-scale social and news data, combining Java microservices, Kafka, Spark, React, and LLM-based retrieval workflows. Stands out for shipping customer-facing AI features with measurable gains in accuracy and latency, plus solid reliability practices for high-volume backend systems.”

“Built and stabilized an AI-assisted document processing workflow for Possible Finance that supported underwriting without automating final loan decisions. Stands out for combining practical LLM integration skills with strong guardrails, validation, and fallback design in a financial workflow, delivering roughly a 35% reduction in document processing time.”

“Built and shipped an end-to-end autonomous telemetry and log-triage product that combined LLM-based anomaly analysis, strict typed validation, and a React observability UI. Particularly compelling is their focus on making non-deterministic AI reliable in production at scale—500,000 daily requests and 99.9% uptime—while also translating complex AI output into a usable experience for non-technical teams during live outages.”

Wei-Hsien Wang

Screened

Entry-level AI Engineer specializing in full-stack generative AI systems

San Jose, CA1y exp

“AI/full-stack product engineer who has shipped both user-facing and internal LLM products, from a photo-to-music recommendation app to an experimentation agent at Azazie. Stands out for combining modern app development with production-grade agent and GraphRAG systems, including a 500k+ email analysis platform and measurable impact like 3x experiment velocity, 75% setup-time reduction, and 65% faster task discovery.”

“Backend engineer with end-to-end ownership experience on a real-time AI-driven payment authorization/orchestration platform at PayPal. They describe strong fintech systems depth across Java/Spring/Kafka microservices, database and latency optimization, and reliability engineering, with concrete impact including 35% fewer processing failures, latency reduced from 420ms to 140ms, 1,200+ weekly manual reviews eliminated, and 40% faster incident response.”

“Backend/platform engineer who has built and run production Python/Flask + Kafka microservices processing RFID and camera/RFID fusion streams for near-real-time retail cart updates at ~4–5M events/day. Strong in reliability/performance debugging (p99 latency, Kafka lag, Cosmos DB RU hot partitions) with measurable impact including ~30% database cost reduction, and has also shipped an end-to-end vulnerability scanning workflow with DynamoDB-backed state, idempotency, and robust retry/verification guardrails.”

“AI/LLM engineer with production experience at Charles Schwab building a RAG-based assistant to help 5,000+ reps answer complex financial policy questions. Implemented a multi-layer anti-hallucination approach (GNN-driven ontology/graph retrieval + citation-only answers) and compliance-focused guardrails (Azure AI Content Safety) in partnership with audit/compliance stakeholders.”

“JavaScript engineer with hands-on experience improving performance in data-heavy table UIs (thousands of rows), including an open-source DataTables extension fix that reduced redundant AJAX calls via debouncing and was merged upstream. Comfortable profiling/benchmarking, optimizing DOM and network behavior, and collaborating with OSS maintainers through GitHub issues/PRs while also producing clear developer documentation and quick-start examples.”

“Software developer with healthcare operations experience at Epic Systems (Referrals & Authorizations), delivering customer-facing tooling to speed manual insurance authorization/denial documentation and support future automation. Also supported an HRIS migration to Workday at Aloe Yoga, solving legacy ID interoperability via scripting and mapping, and demonstrates strong production debugging and test-driven maintainability practices.”

“Full-stack engineer with 3+ years using Java/Spring Boot (Citi) and React, who built a production observability dashboard monitoring 53 microservices across 17 clusters with real-time health/latency tracing and significant performance improvements (cut load time from ~10s). Also designed a serverless AWS face-recognition system (Lambda/S3/SQS) built to handle burst traffic (~1000 concurrent requests), demonstrating strength in scalable, event-driven architectures.”

“Backend-focused engineer who built a Python/Flask task-management API with JWT/RBAC, modular service/repository architecture, and PostgreSQL/SQLAlchemy performance optimizations (indexes, lazy loading, bulk ops, pooling). Also implemented multi-tenant data isolation strategies and built an OpenAI-powered document summarization workflow using chunking, async processing, Redis background workers, and caching to improve throughput.”

“Engineering leader/CTO-type with deep experience building and scaling a vehicle routing platform at Transdev On Demand, including a nationwide rollout to 22 US airports ahead of schedule. Drove engineering best practices (CI/CD, high test coverage, pair programming, automated deployments) and led a multi-tenant architectural upgrade to expand the routing engine to additional business lines and external customers.”

“Engineering leader/player-coach who helped ship a web-based ERP SaaS release (Nov 2025) as part of a long-term migration from a legacy desktop ERP, designing a multi-API architecture (Oracle + EF Core, caching, integrations) and enforcing rigorous code review quality gates. Previously led development of a low-latency, multi-service high-frequency trading platform at a startup hedge fund (Capitalogix Trading), leveraging async/multithreading, event-driven messaging, NoSQL, and WebSockets.”

“Full-stack engineer with end-to-end ownership experience, including building a real-time campaign/inventory dashboard at P&G using React/TypeScript, Spring Boot, GraphQL/REST, Redis, Docker, and AWS (EC2/RDS/S3) with Prometheus/Grafana observability. Demonstrates strong performance and reliability focus (p95 tuning, caching, idempotent event-driven ingestion with DLQs/reconciliation) and has shipped MVPs in ambiguous early-stage environments.”

“AI/software engineer with experience at Visa building a real-time transaction fraud/risk scoring microservice in the card authorization path (Python, Kafka, Kubernetes on AWS) with strict 120–150ms latency constraints and reason-code outputs for downstream decisioning. Owns ML backend end-to-end (data/feature engineering, model training, deployment) and has demonstrated production reliability work including latency spike mitigation, SLO-based observability, drift monitoring, and safe fallbacks to rule-based decisions.”

“Unreal Engine UI engineer focused on scalable, production-ready UI architecture (C++/Slate/UMG/CommonUI) with strong designer enablement via decoupled, interface-driven patterns and MVVM. Demonstrated measurable performance wins: replaced 200+ per-frame Blueprint bindings to cut UI prepass/paint from 4.2ms to 0.5ms and reduced VRAM by ~120MB using texture streaming proxies.”

“Full-stack engineer with production experience building a security integrations portal in Next.js (App Router + TypeScript), using server components and typed route handlers as a secure proxy to multiple third-party security vendors. Demonstrated ability to scale performance significantly (server-side re-architecture for 1M+ datapoint dashboard filtering; Postgres query tuning from 1–2s to <200ms) and to own features post-launch (reliability, caching/background sync, and rapid onboarding of new integrations) in a pre-Series B through post-Series C environment.”

Pranav Chand

Screened

Senior AI/ML Engineer specializing in Generative AI and LLM platforms

ServiceNow, CA5y exp

“Backend engineer focused on multi-tenant enterprise AI personalization and recommendation platforms, combining ML/LLM intent extraction with deterministic policy guardrails for compliance and auditability. Has hands-on AWS experience (ECS/Lambda/DynamoDB/S3) and led a careful DynamoDB single-table migration using dual write/read, canary + feature-flag rollouts, and strong observability/security (JWT/OAuth2, RBAC, Postgres RLS).”

Harshavardhan Reddy

Screened

Mid-level AI/ML Data Scientist specializing in NLP, computer vision, and risk analytics

Albany, NY5y exp

“ML/AI engineer with Capital One experience building production-grade customer segmentation and fraud detection systems combining NLP (transformers) and anomaly detection. Strong MLOps and orchestration background (PySpark ETL, MLflow, Airflow, Docker/Kubernetes, Azure ML) with real-time monitoring/alerting and performance optimizations like quantization and caching, plus proven ability to deliver business-facing insights through Power BI/Tableau for marketing stakeholders.”

Naga Manasa Bandaru

Screened

Mid-level Frontend Software Engineer specializing in React, Next.js, and TypeScript

Dallas – Fort Worth, TX4y exp

“Product-focused full-stack engineer with FedEx experience building an internal logistics dashboard for near real-time shipment status and performance metrics using Next.js App Router + TypeScript. Strong in production ownership and performance work—uses React Profiler/Chrome DevTools to eliminate expensive re-renders and applies Postgres indexing/query tuning validated via EXPLAIN ANALYZE to improve dashboard responsiveness.”

Vaibhav Sharma

Screened

Mid-level Software Engineer specializing in AI/ML and data platforms

Remote, USA5y exp

“AI/ML engineer who built a production agentic system to automate computational research experiments (simulation execution, parameter exploration, and numerical analysis) and mitigated context-window failures using constrained tool-calling/prompt-chaining patterns in LangChain with OpenAI tool-enabled models. Also has adtech/big-data pipeline experience at InMobi, orchestrating Spark jobs in Airflow to filter bot-like user IDs and publish clean IDs to an online NoSQL store for live serving, plus Apache open-source collaboration experience.”

Alexey Elkin

Screened

Junior Full-Stack Software Engineer specializing in cloud analytics and web applications

New York, NY2y exp

“LLM/agentic workflow engineer with hands-on experience turning demo-grade LLM analytics into production-ready features by tackling tail latency, observability, and cost/reliability controls. Strong at diagnosing real-time customer incidents via trace-driven debugging across retrieval, tool calls, retries, and prompt/version metadata, and frequently partners with sales as a technical lead to de-risk enterprise pilots through transparent failure-mode walkthroughs.”