Vetted Latency Optimization Professionals

“Fintech-focused mobile engineer who built and shipped a mobile wallet to both iOS and Android app stores, implementing biometric login and AI-driven KYC (face + ID verification). Demonstrates strong customer feedback loops and production problem-solving, including resolving iOS version-specific third-party AI integration issues and improving payment UX by moving from synchronous to asynchronous processing.”

“Data science professional who has built a production RAG-based LLM question-answering system ("Flash Query") to deliver fast, accurate answers over large document collections, focusing on retrieval quality and grounded responses. Also collaborates with non-technical retail/jewelry stakeholders to turn business questions into predictive models and dashboards for decision-making.”

Ttanvi Tummapudi

Screened

Junior Data Scientist specializing in machine learning, predictive modeling, and applied AI research

2y exp

“Data scientist/researcher who has built two multimodal LLM systems: an AI-assisted medical triage pipeline using GPT-4o vision + RAG with confidence-scored red/yellow/green outputs, and a master’s project on multimodal cyberthreat detection combining multiple models and using TinyLlama to generate human-readable risk reports. Also partnered with business analysts at Sanvar Technologies to deliver a churn prediction pipeline and Tableau dashboard for decision-making.”

“Lead developer on PoolLiveTourMax who owned core gameplay, physics, backend services, and end-to-end multiplayer architecture. Brings a strong blend of Unity gameplay engineering and backend/networking expertise, including server-authoritative netcode, dedicated server deployment, and performance optimization for memory, FPS, and latency.”

“Ingeniero de Robótica y Automatización con 10 años de experiencia en instalaciones robóticas (principalmente sector aeronáutico) y proyectos de investigación en UK. En una gran empresa ferroviaria programó desde cero una instalación con 3 robots Staubli con tracks y desarrolló una plataforma de virtualización/simulación en ROS 2 (Gazebo/MoveIt 2), incluyendo integración industrial OPC UA/Profinet con PLC para 21 ejes y visualización en tiempo real.”

Taruni Reddy Ampojwala

Screened

Mid-level GenAI Engineer specializing in LLM agents and RAG systems

Brooklyn, NY4y exp

“Built and deployed a production RAG-based LLM assistant that answers day-to-day operational questions from internal PDFs/SOPs, with strong emphasis on data consistency (metadata versioning, confidence thresholds, conflict handling) and low-latency retrieval at scale. Experienced designing and orchestrating multi-agent LLM workflows (retrieval/validation/generation) and pipeline orchestration for ingestion/embedding/vector-store updates, plus iterative delivery with non-technical operations/business stakeholders.”

Mayank VYAS

Screened

Mid-level AI/ML Engineer specializing in LLM agents, RAG retrieval, and IoT ML systems

Tempe, AZ4y exp

“Built production LLM-driven products including a job-hunt AI (job ranking + resume optimization) and an InterviewAI agentic pipeline using LangChain. Focused on practical deployment concerns like securing OpenAI usage via rate limiting and tiered quotas, and demonstrates an applied approach to choosing models, retrieval methods (RAG), and prompting strategies.”

“Indie/solo Unity C# developer who shipped a VR game (Elysium Trials) across Meta Quest, SteamVR, and Pico, including a high-performance cross-platform leaderboard system with sub-second score retrieval. Also built a recent multi-agent AI puzzle project where multiple AI bots communicate via JSON and uses UX techniques to mask LLM latency; has shipped additional mobile titles and focuses heavily on optimization and immersive VR UI.”

“Unity developer focused on VR/AR multiplayer, with hands-on experience building Photon Fusion/PUN systems including networking, sync, and matchmaking. Has scaled a modular multiplayer architecture to handle more players/features while addressing latency and data consistency, and has optimized Meta Quest/mobile performance using pooling, LODs, batching, and profiling.”

“Built and shipped "OnCall Copilot," a production Slack-based RAG assistant that answers on-call questions from runbooks and postmortems with citations using a FAISS vector index. Emphasizes reliability and measurable performance via strict guardrails ("no evidence, no answer"), evaluation metrics, drift monitoring, and operational hardening with Docker, logging, health checks, and offline fallback.”

“Built and deployed a "personal second brain" product (CloneMind) with an end-to-end RAG pipeline for retrieving information across PDFs, URLs, images, and audio using Next.js/Node.js/Postgres/Supabase/Redis. Demonstrates strong practical depth in retrieval quality tuning, latency reduction via caching, and stateful orchestration with LangChain/LangGraph, plus experience persuading a non-technical professor stakeholder by shipping a working prototype.”

“Software engineer (internship experience) who built and owned an AWS serverless multi-user “challenge” feature end-to-end (UI + REST APIs + DynamoDB + deployment), delivering measurable gains in latency (-30%), debugging time (-50%), and join drop-offs (~-30%). Also productionized a multilingual RAG-based QA system with vector retrieval and guardrails, improving accuracy to ~85% and driving ~20% DAU growth.”

CharanKumar Pathakamuri

Screened

Entry-Level GenAI/LLM Engineer specializing in agentic systems and RAG

Baltimore, MD1y exp

“LLM/AI agent engineer with consulting/contract experience (Kanhaiya Consulting LLC) who deployed a production AI agent to automate BIM list workflows end-to-end—from database understanding and data cleaning to automated visualizations/dashboards. Worked around restricted real-time data access by generating synthetic data and improving outputs via supervised fine-tuning, and uses AWS-based LLMOps observability (Opic/OPEC) plus hybrid retrieval (vector+BM25 with reranking) to optimize relevance, latency, and cost.”

Ajay Kudapa

Screened

Mid-level Software Engineer specializing in Healthcare IT and Web3

4y exp

“Software engineer with experience spanning healthcare middleware (patient records + insurance integration) and an AI fantasy football product built with React/TypeScript, Firebase, API gateways, and pandas-based data pipelines. Has hands-on microservices scaling experience (latency mitigation, async migration, state-based redesign) and built an internal feature-toggle dashboard that improved demo efficiency and sales outcomes.”

“Unity mixed-reality developer who shipped ZenPlay, a multiplayer Go app on Meta Quest, integrating a C# rules engine with XR input, Meta avatars, Hathora-hosted matches, and Vivox voice chat (reported ~700 MAU). Also built a production LLM agents backend (LangChain + RAG with Pinecone/ChromaDB + ChatGPT) powering embodied conversational avatars, with a strong focus on streaming voice latency optimization (ElevenLabs TTS) and cross-platform WebXR delivery (Quest/iOS/Android).”

“Built a production multi-agent RAG assistant using LangChain/LangGraph with OpenAI embeddings and FAISS, focusing on retrieval quality and latency (Redis caching, parallel retrieval, precomputed embeddings). Experienced orchestrating ETL/ML pipelines with Airflow and Databricks Workflows, and has delivered an AI assistant for business ops to extract insights from policy/compliance documents through close non-technical stakeholder collaboration.”

“Built and shipped production LLM agents for real-time, high-concurrency conversational systems, including a RAG-based pipeline with dynamic multi-provider routing and failover that achieved 99.99% reliability and sub-800ms latency. Also architected a UAV telemetry chatbot with tool-calling (anomaly detection/summarization), strict schema validation, and robust eval/monitoring loops, cutting tool-call errors by 30% and reducing operational costs by 90%.”

Asad Abdul

Screened

Mid-level Full-Stack Developer specializing in AI-driven cloud-native applications

Laguna Niguel, CA4y exp

“Full-stack engineer with healthcare/ops analytics experience at PatientXpress, shipping a real-time operational dashboard end-to-end (React/TypeScript + Node/Postgres on AWS) that cut manual reporting by 50%. Strong in performance and reliability work—pagination/caching, Postgres indexing/partitioning, Terraform-based AWS provisioning, CI/CD with GitHub Actions, and production incident response with improved monitoring (CloudWatch/Prometheus).”

“AI/ML engineer with hands-on experience owning production deployments from discovery through post-launch stabilization, including real-time computer vision/OCR systems and LLM-powered RAG workflows. Stands out for translating messy customer workflows into reliable backend services, debugging non-deterministic retrieval issues, and hardening AI systems with validation, monitoring, and human-review fallbacks.”