Vetted Data Structures and Algorithms Professionals

“Worked at AMD on Vivado tool releases, focusing on adding and integrating new IP/SoC functionality into the Vivado flow and validating expected behavior through testing. Comfortable engaging with customers and open to travel for hands-on customer-facing work.”

“Built and shipped an internal Financial Insights Assistant for banking analysts, owning the experience from workflow design through React frontend, FastAPI backend, and AI search integration. Particularly strong in making AI products usable and trustworthy by surfacing sources, experts, suggested prompts, and search history to improve confidence and speed.”

“Open-source contributor who improved React Query’s caching/subscription behavior to reduce unnecessary re-renders via debouncing and batched updates, validated with benchmarking and extensive tests. Also maintained a Flask extension and resolved production background-task hangs by tracing Redis connection handling issues, adding cleanup/retry logic and troubleshooting docs. In a fast-paced startup, owned the design of a Celery+Redis multi-queue background processing system with Prometheus-based observability.”

“Built an autonomous decision analytics LLM agent for end-to-end tabular binary classification, using RAG (FAISS) to retain context across multi-step queries. Deployed as a FastAPI service with production-style reliability features (schema-aware validation, fallbacks, retries, structured outputs) plus offline/online evaluation and monitoring to reduce analysis time and improve consistency versus stateless approaches.”

“Data engineer/backend engineer with experience in healthcare (Cardinal Health provider enrollment) and finance (Northern Trust) building and stabilizing data pipelines and REST services. Worked with APIs and Kafka at ~200k–300k records/day, improving data quality (DLQ + validation), performance (SQL/indexing), and reliability/observability (logging, alerts, consumer lag metrics), and stood up an early-stage financial data service with Jenkins-based CI/CD.”

Ramtin Kazemi

Screened

Junior Full-Stack Software Engineer specializing in Django, AWS, and AI/ML

San Diego, California1y exp

“Full-stack engineer who built and owned an AI-powered personal statement editor in Next.js (App Router + TypeScript), including dynamic routing, server-side data fetching, and typed API route handlers. Post-launch, they handled production monitoring/debugging and shipped reliability/performance upgrades (rate limiting, retries, rollback, DB indexing), and report a 40% latency reduction using Suspense/streaming and React concurrency patterns. Also implemented a durable Temporal-orchestrated AI document workflow with robust retry/idempotency strategies.”

Mukul Sai Pendem

Screened

Mid-level Full-Stack Engineer specializing in cloud-native microservices and DevOps

United States (Remote)4y exp

“Backend engineer with strong Python/FastAPI microservices ownership, including an ML-serving service with embeddings, async DB access, and Redis caching to reduce latency under high load. Experienced deploying and operating containerized services on Kubernetes using GitOps (Argo CD/Helm) with automated CI/CD, plus hands-on Kafka streaming pipeline tuning and enterprise migration work (Infosys) using blue-green/active-passive strategies.”

“Frontend-focused builder who developed Lushgreen, an ML-powered harvest forecasting and recommendation platform for farmers, agricultural businesses, and home gardeners. Stands out for combining React UI engineering, performance optimization, and user-centered simplification of complex ML outputs for non-technical users.”

“Robotics software developer building an ESP32/FreeRTOS wheel-encoder driver for a differential-drive robot with PID velocity control and ROS2 odometry publishing. Has implemented vision-based human tracking (DepthAI) and an obstacle map + EKF sensor fusion using IMU and LiDAR, with a simulation-first debugging workflow (Gazebo, Foxglove) and Dockerized environments; plans to extend to Nav2 + SLAM Toolbox for building-scale mapping.”

Naga Renuka Kandi

Screened

Junior Software Engineer specializing in cloud, full-stack development, and Generative AI

Remote, USA2y exp

“Built and shipped a production Chrome extension (Promptly) that lets users select text on any webpage and transform it in place (rewrite/shorten/translate) using on-device AI plus external LLMs. Implemented a custom lightweight orchestration layer for prompt chaining, context flow, and output validation, and tackled tricky browser Selection API issues to preserve formatting while keeping the UX simple and fast.”

“Candidate appears to work at the intersection of enterprise billing/payments systems and AI-powered support automation. They describe owning customer deployments, integrating PayPal/Stripe, building LLM/RAG workflows for finance operations, and handling production incidents affecting millions of invoice events with measurable improvements in resolution time and ticket volume.”

Dikshith Pulakanti

Screened

Intern AI Engineer specializing in agentic LLM systems

Singapore, Singapore0y exp

“Built multiple AI-heavy backend systems from scratch, including FORESIGHT, a personal financial intelligence platform running daily on live bank accounts with zero manual intervention, and JobPilot, an autonomous job application agent spanning Workday, Greenhouse, Lever, and custom forms. Stands out for combining strong systems design with applied ML pragmatism, reproducibility, and unusually candid reflection on security, scalability, and observability tradeoffs.”

Nithyashree Raghunathan

Screened

Mid-level Full-Stack AI Engineer specializing in agentic systems

Santa Clara, CA5y exp

“QA/data pipeline engineer with hands-on AI product building experience, spanning enterprise AWS migration testing for Belgium postal services and personal multi-agent systems in fintech and recruiting. Stands out for combining rigorous validation and production stability work with modern LLM orchestration, guardrails, and messy-document normalization workflows.”

PrabhjyotKaur Bamrah

Screened

Mid-level Full-Stack Software Engineer specializing in enterprise apps and AI integration

Redwood City, CA4y exp

“Engineer with experience scaling enterprise AI products in production, including a C3 AI deployment expanded from 2 to 4 sites across the US and Canada. Also built a GPT-4o-powered RAG assistant for plant operators, combining structured and unstructured data with human-in-the-loop safeguards and iterative evals to improve answer quality.”

“Candidate indicated they live in New York but did not provide substantive details about their experience; they declined to discuss at least one topic as restricted and did not answer role-related questions with concrete examples.”

“Full-stack engineer with strong production ownership who built a "Problem Workspace" coding feature using Next.js App Router + TypeScript, combining Server Components for fast initial render with WebSocket-driven real-time execution updates. Demonstrates deep reliability and data-consistency expertise (idempotency keys, Postgres constraints/indexing, EXPLAIN ANALYZE) and has implemented durable async orchestration (Temporal-style workflows) to reduce failures and timeouts under load.”

“Backend engineer with hands-on experience designing and refactoring scalable Node.js/MongoDB systems and building Python/FastAPI services. Emphasizes production-grade security (JWT, refresh tokens, RBAC, Supabase Auth, RLS) and reliability practices like strong testing, monitoring, and rollback planning, including resolving concurrency and token/validation edge cases.”

“Built and productionized LLM-driven content intelligence/SEO agents for a high-traffic media platform, automating tagging/summarization/metadata with FastAPI + async orchestration and strict JSON-schema outputs. Demonstrated measurable impact (40% faster publishing, +20% organic traffic in 3 months) and strong reliability practices (offline evals, shadow mode, canaries, fallbacks, idempotency, and monitoring).”

Yuvraj Singh Chauhan

Screened

Entry-level AI/ML Engineer specializing in LLMs, RAG, and DevOps automation

Bangalore, India1y exp

“Built and owned a production-scale AI-driven software release/version intelligence platform orchestrated via GitHub Actions that tracks 1000+ upstream repositories and automatically generates SLA-bound JIRA upgrade tickets for hardened container images. Replaced brittle regex/PEP440 parsing with an LLM-based semantic filtering layer plus deterministic validation to handle noisy/inconsistent GitHub tags at scale, with monitoring for coverage, latency, and correctness validated against upstream ground truth.”

Connor White

Screened

Junior Machine Learning Engineer specializing in GPU-accelerated computer vision

College Station, TX5y exp

“Robotics software lead from Texas A&M Aggie Robotics who built WoopLib, a SLAM-based vision/navigation library using PID pure pursuit. Has hands-on ROS/ROS2 and Jetson Nano experience integrating Intel RealSense (T265/D435i) with wheel odometry for accurate state estimation, including compiling deprecated sensor support from source and optimizing by moving to Python with C++ bindings and serial streaming to a microcontroller.”

Nikhitha santhoshi Maroju

Screened

Mid-level Full-Stack Software Engineer specializing in Java/Spring microservices on AWS

Redwood, CA4y exp

“Built and shipped a production LLM-powered fraud investigation agent using RAG to generate transaction explanations and draft analyst reports. Emphasizes production robustness (fallbacks, strict structured outputs, async orchestration, monitoring/evals) and reports measurable impact: ~12% precision lift and ~80 high-priority alerts per week with reduced manual effort.”

Yuchen Wang

Screened

Intern Software Engineer specializing in full-stack development and AI/ML

New York, NY1y exp

“Built and maintains an AI Finance Tracker end-to-end as a solo full-stack product owner, from Figma designs and React frontend to Flask APIs, Firestore, auth, deployment, and AI insights. Stands out for combining product instinct with pragmatic engineering decisions like pre-aggregating financial data to control LLM costs and adding OCR receipt scanning based on real user feedback.”

“Software engineer with 4 years of experience spanning scalable backend systems, full-stack product development, and production LLM integrations in finance, insurance, and e-commerce contexts. They describe shipping an AI-powered internal financial analysis tool, improving document-review workflows by 40%, and driving a zero-to-one B2B SaaS subscription launch with cross-functional GTM alignment.”

“Built and owned an end-to-end RAG-based AI support platform at Mechanize (FastAPI/LangChain/Pinecone/React) with rigorous evals and guardrails, driving 45% fewer support tickets and ~$280K annual savings. Also led a high-risk legacy modernization at Argo AI, incrementally extracting a monolithic Django backend using Strangler Fig + feature flags while supporting 10K+ concurrent users.”