Vetted Distributed Systems Professionals

“LLM/RAG-focused engineer who repeatedly takes agentic workflows from impressive demos to dependable production using rigorous evals, SLOs, and deep observability. Has led high-impact incident mitigation (22-minute MTTR during a major sale) and developer enablement workshops, and partnered with sales to close a $410k ARR enterprise deal with a tailored RAG pilot (FastAPI/pgvector/Okta/InfoSec-ready).”

“Backend developer (recent co-op at Ticker) building and architecting financial backend services with near real-time data needs, including third-party API integrations. Improved performance and reliability via Redis caching (tiered refresh + TTL) and PostgreSQL query tuning (EXPLAIN ANALYZE + composite indexes), and has exposure to AI-agent/RAG concepts for validating stock-market information against trusted sources.”

“AI research engineer who has built and tested LLM agents end-to-end, including a Telegram real-time voice-to-typing assistant integrated with calendar scheduling. Emphasizes production concerns (security via mic-triggered activation, multi-model fallbacks, monitoring) and agent predictability using a GPT-3.5-based critic plus structured outputs (Pydantic) and ReAct-style orchestration.”

“Full-stack product engineer who builds and owns production features across Next.js/React/TypeScript and Java Spring Boot, with strong Postgres data modeling and performance tuning. Has delivered measurable improvements (60%+ faster renders, 2s→100ms queries, 50% lower workflow latency) and built reliable Kafka-based workflows with robust observability (Prometheus/Grafana/Alertmanager) and high test coverage.”

Likhith Kumar Yaramala

Screened

Junior Backend/Cloud Software Engineer specializing in microservices and DevOps

India2y exp

“Cloud/DevOps-focused engineer with strong Linux production operations experience, deploying microservices to AWS on Docker/Kubernetes. Has built and operated secure CI/CD (GitHub Actions/Jenkins) and Terraform IaC workflows with approvals, remote state, and drift detection, and has hands-on incident recovery experience in containerized environments; limited direct IBM Power/AIX/PowerHA exposure.”

Abhishek Ingle

Screened

Junior Full-Stack & AI Software Engineer specializing in React/Next.js and LLM systems

Bloomington, IN2y exp

“Backend engineer with hands-on experience building low-latency, high-concurrency real-time chat on AWS (Node.js/Socket.IO/MongoDB) and improving reliability under unstable networks, contributing to ~40% user adoption growth. Also built FastAPI-based AI assistant context retrieval (RAG) APIs with embeddings/vector search, and has strong production experience in rate-limit handling, async refactors with safe rollout, and Supabase Auth/RLS optimization.”

Thira Patel

Screened

Junior Robotics/Mechatronics Engineer specializing in SLAM, motion planning, and autonomy

1y exp

“Robotics software engineer focused on autonomy stacks for high-payload AMRs using ROS2/Nav2, with hands-on expertise in SLAM/localization and sensor fusion (RTK GPS, IMU, wheel odom, ZED2) to eliminate drift and stabilize real-time behavior on deployed hardware. Also built multi-robot coordination in ROS2/Gazebo and uses Docker + Git/CI-style testing to create reproducible simulation-to-hardware pipelines.”

Thanmayi Ravuri

Screened

Entry Software Engineer specializing in cloud backend and microservices

Tempe, AZ1y exp

“Built production-oriented LLM agent systems for incident investigation and CRM workflows using LangGraph, FastAPI, AWS, and retrieval grounding. Stands out for treating agents like real software systems—adding schema enforcement, retries, fallbacks, monitoring, and eval loops—and tying that work to measurable gains in accuracy, latency, and analysis speed.”

Raunak Bandyopadhyay

Screened

Intern-level Full-Stack Engineer specializing in AI and web systems

Amherst, MA0y exp

“Backend-focused intern in telehealth who combined engineering, QA leadership, and emergency infrastructure ownership. Built a text chat feature and an OpenAI-powered therapy chatbot, then stepped up to rebuild AWS infrastructure after it was accidentally deleted, while also improving chat responsiveness and reporting a 30% engagement lift.”

Nicholas Shellabarger

Screened

Senior Software Engineer specializing in distributed FinTech systems

Denver, CO13y exp

“Founder of an early-stage startup who has already raised initial capital through a SAFE and built the platform with minimal funding. Demonstrates practical knowledge of VC and accelerator fundraising, a deliberate equity-preservation strategy, and is now seeking additional financing to create runway and scale the business.”

Meghana Divve

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native enterprise applications

Tampa, FL6y exp

“Built and launched a production internal AI support assistant at CompuCom, focused on reducing time spent searching across systems by combining retrieval, internal tool use, and grounded LLM responses. Stands out for pragmatic zero-to-one execution: scoped the product in phases, prioritized safety over premature autonomy, and iterated using real user feedback to improve relevance, usability, latency, and cost.”

Raghava Sammeta

Screened

Mid-level Full-Stack Engineer specializing in cloud, microservices, and AI systems

Atlanta, GA4y exp

“Built and owned Destination Together, a collaborative travel platform, end-to-end across frontend, backend, data, infrastructure, and payments. Stands out for combining React/Node microservices, PostgreSQL concurrency design, real-time messaging, Dockerized deployment, and Gemini-powered route-aware travel suggestions in a single product.”

“Full-stack engineer with recent hands-on work across scientific data infrastructure and enterprise analytics. Notably drove a Crossref-to-DataCite DOI migration at CUAHSI, owning metadata mapping, Django integration, ROR funder handling, and safe production rollout, and has contributed broadly to HydroShare, a research data platform used by thousands of researchers.”

“Full-stack engineer with healthcare/AI platform experience (Humana), owning an end-to-end high-risk patient prediction feature from React dashboards through FastAPI/TensorFlow real-time inference to AWS EKS operations. Emphasizes production reliability and contract-driven APIs (OpenAPI + generated TS types), plus strong data integration patterns (Kafka, idempotency, DLQs, backfills) in regulated, high-traffic environments.”

“Robotics simulation/localization engineer who built a lunar crater navigation stack in ROS/ROS2 and Gazebo, including custom localization/perception/planning packages. Demonstrated strong debugging skills by using tf2 frame analysis to fix camera-to-base_link alignment, cutting heading error from 75° to 0.48°, and handled large NASA lunar imagery (~4GB) by converting/downsampling data for Gazebo.”

“Built a production LLM/RAG document extraction and game/quiz content workflow using LLaMA 2, LangChain/LangGraph, and FAISS, achieving ~94% accuracy and reducing turnaround from hours to minutes. Demonstrates strong applied MLOps/orchestration (CI/CD, MLflow, Databricks/PySpark), robust handling of noisy/variable document layouts (layout chunking + OCR fallbacks), and practical reliability practices (human-in-the-loop routing, drift monitoring, A/B testing).”

“Backend/data engineer with production experience in financial payroll, tax, and compensation platforms, building Python microservices and AWS-based data pipelines for high-volume, peak-driven workloads. Strong reliability focus (OAuth2 auth, retries/timeouts, structured logging, incident response) and proven performance wins, including cutting complex report queries from ~8 minutes to under 30 seconds.”

“New-grad backend engineer who built a real-time genome analysis pipeline, replacing a slow batch system with an event-driven distributed architecture in Python/Redis and a React progress dashboard. Reports ~6x improvement and cutting analysis time from days to hours with zero data loss under peak load, emphasizing reliability patterns like retries and idempotency plus API security (JWT/RBAC/HTTPS).”

“Backend/AI engineer with internship experience building an AI-powered financial insights platform (FastAPI, Redis, BigQuery) and prior HCL experience leading a monolith-to-microservices refactor (Flask, Kafka) using blue-green deployments. Demonstrates strong performance/security focus (OAuth/JWT/RBAC, encryption) and measurable impact on latency, downtime, and ML model reliability; MVP was submitted to Google’s accelerator program.”

“At Unar Labs, built and operationalized LLM capabilities inside a cloud-native SaaS product, emphasizing production reliability (fallbacks, observability, cost/latency/quality monitoring) and iterative improvement from user feedback. Also acts as a customer-facing technical lead—running developer demos/workshops and supporting sales through discovery, pilots/POCs, and technical walkthroughs to drive production adoption.”

“Founder/CTO-type candidate with deep exposure to the VC/accelerator ecosystem (Open Angel Forum; HAX/SOSV) and a track record of raising capital, including a $1M seed round in 2017. Built ventures spanning IoT wearable/HUD SaaS for industrial hygiene monitoring and institutional Bitcoin mining/distributed compute infrastructure, and ran a venture from 2017–2025.”

“Backend/full-stack engineer who has owned production TypeScript systems in both fintech-style transaction/rewards flows and HIPAA-regulated healthcare platforms. Deep focus on correctness and reliability (idempotency, retries/DLQs, reconciliation, observability) plus strong infra automation (Docker/Terraform/CI-CD) and measurable performance wins (40% query improvement, 90% test coverage).”

Anudeep Reddy Veerla

Screened

Mid-Level Software Engineer specializing in cloud-native microservices

Boston, MA3y exp

“Built and shipped both a solo real-time multiplayer Spades game (TypeScript monorepo with shared client/server engine) and a production internal LLM-powered document Q&A tool for a SaaS company. Demonstrates strong RAG pipeline design (Pinecone + embeddings + reranking), rigorous eval/regression practices, and pragmatic data ingestion/observability work across Confluence, Notion, and messy PDFs/OCR—backed by clear metric improvements (P@1 61%→78%, escalations 40%→22%).”