Vetted Distributed Systems Professionals

Dhairya Shah

Screened

Entry-level Machine Learning Engineer specializing in computer vision and systems

Buffalo, NY1y exp

“ML-focused builder who has shipped an end-to-end income-class prediction product: built the data pipeline, trained models, deployed via Streamlit with a live UI, and tracked success via accuracy (84%), adoption, and latency. Demonstrates strong practical MLOps instincts (Docker/Streamlit Cloud, logging/monitoring, caching) and data engineering reliability patterns (schema checks, idempotency, retries, backfills) while iterating quickly in ambiguous, solo-project environments.”

Iskhak Ishmakhametov

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and real-time systems

Bellevue, WA7y exp

“Full-stack product engineer with a strong real-time systems focus: built and rolled out a WebSocket-based notifications system (with robust reconnect/resync and event ordering protections) that cut update latency to under 200ms. Also owned a workflow automation platform backend in FastAPI (JWT/RBAC, versioned APIs, standardized errors), designed the PostgreSQL schema for workflows/tasks/executions, and operated deployments on AWS ECS Fargate with blue-green CI/CD and performance stabilization via caching and autoscaling.”

Haneesh Kapa

Screened

Junior AI Full-Stack Engineer specializing in LLM automations and RAG systems

Nashua, NH2y exp

“Built and shipped a production LLM-powered customer support assistant using a Python/FastAPI backend with RAG (embeddings + vector search) over internal docs and product/operational data. Instrumented the system with logging/metrics and ran continuous eval loops; post-launch improvements focused on retrieval quality (chunking/ranking) and performance/cost tradeoffs (query classification, caching, validation guardrails).”

“Machine learning lab assistant at Eastern Illinois University who productionized a voice-enabled conversational AI system: redesigned it with RAG, LoRA fine-tuning (including text-to-SQL), and safety guardrails, then deployed a scalable API supporting ~1,000 daily queries. Also partnered with customer-facing teams during a BlueFi internship by building demos/APIs and accelerating releases via Terraform + AWS CI/CD automation.”

“Software engineer with demonstrated production performance wins (37% latency reduction) through SQL optimization, backend API redesign, and disciplined rollout practices (staging, feature flags). Experienced debugging distributed pipeline issues across infrastructure layers (memory pressure and network timeouts) and building AWS-based systems (Lambda + RDS) to handle request spikes, including work on a business-focused chatbot.”

“Backend Python engineer who owned a real-time manufacturing insights streaming service, building FastAPI async microservices with Kafka-style queue buffering, batching/backpressure, and a low-latency snapshot store. Led a serverless-to-Kubernetes (EKS) migration at UGenomeAi using GitOps-style GitHub Actions pipelines, standardized config/secrets, and improved deployment consistency with pinned dependencies and multi-stage Docker builds.”

“Full-stack system analyst/programmer at PeakPlay Sports (startup) who built an AI "coach" product end-to-end in ~2 months, using a LangGraph-orchestrated multi-agent architecture with a FastAPI backend. Shipped production RAG grounded in athlete history (OpenAI embeddings + vector store) with guardrails and a structured eval loop (golden set + LLM-judge + human review) to improve engagement and reduce hallucinations.”

Sai Vamsee Bandaru

Screened

Entry-level Data Analyst and AI Engineer specializing in machine learning and LLM systems

New York, NY1y exp

“Founding-engineer-oriented full-stack product engineer who built an AI tutor system end-to-end, spanning React UI, FastAPI backend, retrieval/LLM pipelines, and Postgres optimization. Stands out for combining product thinking with deep systems work: improving onboarding and activation, shipping quickly with beta users, and abstracting reusable retrieval infrastructure for multiple use cases.”

“Robotics software engineer focused on reliability in real-time sensor pipelines and ROS/ROS2 integration, with hands-on experience hardening systems against noisy data, dropouts, and network variability. Uses ROS introspection tools plus simulation (Gazebo/Webots) to diagnose latency and stability issues before hardware deployment, and supports repeatable rollouts via Docker and CI/CD.”

Lalith Sagar Kambala

Screened

Junior Full-Stack Software Engineer specializing in AI-powered SaaS

United States, USA1y exp

“Worked on an AI-adjacent search/results product with a React front end and an API-driven backend, focusing on scalability and performance. Emphasizes decoupled JSON API architecture, React rendering optimizations (useMemo/useCallback), and large-dataset techniques like virtualization, plus strong user-issue triage via log analysis and edge-case fixes in query handling/ranking.”

“Internship-built full-stack systems spanning HR employee-record portals and internal data-quality dashboards (Flask + SQL + React), emphasizing data integrity and rapid MVP iteration. Also implemented Flask microservices with RabbitMQ for distributed task processing, addressing duplication/ordering issues with idempotency, durable queues, and correlation-ID logging; delivered quantified productivity gains for HR teams.”

“Python backend developer from Larix Technologies who built and scaled microservice APIs for an omnichannel messaging SaaS (WhatsApp/Instagram/Facebook) and led production performance fixes during peak traffic, cutting webhook latency ~50%. Also shipped applied AI products end-to-end: a RAG-based PDF assistant (LangChain + Mixtral via Groq + React) and a BI agent that plans/executes/verifies multi-step analytics with strong guardrails and auditability.”

John Kenny

Screened

Senior Full-Stack Engineer specializing in Web3 and FinTech

Global, Remote6y exp

“Full-stack engineer with deep startup experience (pre-seed through IPO/SPAC) currently building a Next.js/TypeScript SaaS sports analytics platform with a complex Postgres-based entitlement/ACL system. Has delivered measurable UX/business impact (35% retention lift, 40% volume increase) and built production-grade daily ETL + model training/inference workflows with validation and checkpointing for reliability.”