Vetted Research Assistants

“Built an AI-powered stylist / outfit recommendation product end to end, spanning React/TypeScript frontend, Postgres data modeling, serverless backend flows, and LLM-driven recommendation/explanation systems. Stands out for combining hands-on full-stack execution with strong product judgment around ambiguity, UX polish, reusable primitives, and AI trust/explainability.”

“Robotics software engineer with hands-on ROS (ROS 1) experience building sensor-processing and state-based control pipelines in Python/C++. Demonstrated measurable reliability and performance gains in autonomous navigation—cut runtime failures by 30%, reduced replanning by 35%, and improved debugging efficiency by 40%—using timing-aware state machines, message/interface discipline, and simulation/testing with Gazebo, rosbag, Docker, and CI/CD.”

“Built and shipped a production LLM-powered decision system that replaced a slow, inconsistent manual review process by turning messy text into structured, auditable outputs behind an API. Demonstrates strong end-to-end ownership of reliability and operations (schema validation, retries/fallbacks, latency/cost controls, monitoring for drift) and a disciplined approach to evaluation and regression testing. Experienced collaborating with non-technical reviewers to define success criteria and deliver interpretable outputs that get adopted.”

“Robotics software engineer focused on ROS 2 multi-robot coordination, having built task allocation and reliable inter-robot communication for simulated TurtleBot3 fleets. Demonstrates strong integration/debugging skills across Nav2 + gmapping (SLAM drift, TF tree, odometry/sensor fusion) and pairs it with production-minded tooling—Docker/Kubernetes deployments and CI/CD simulation testing via GitHub Actions.”

Jonathan Grebe

Screened

Mid-level Robotics Software Engineer specializing in ROS, C++ and embedded Linux

Ottawa, Canada7y exp

“Robotics software lead at Icor who grew from intern to owning the end-to-end software lifecycle for a mobile manipulator platform deployed to 300+ customers globally. Deep hands-on ROS2/MoveIt2 and navigation-stack integration (URDF/TF, sensors, behavior engine) plus production infrastructure (CI/CD, OTA, field OS upgrades) and real-world performance tuning for motion planning in EOD multi-robot environments.”

Shivaram Srikanth

Screened

Junior Robotics Engineer specializing in computer vision and sensor fusion

Mountain View, CA2y exp

“Robotics software engineer with ~3 years of ROS experience spanning drone autonomy and perception. Recently improved drone barcode scanning by shifting to segmentation and deploying an optimized instance-seg model to edge hardware (FP16 quantization, convex-hull masks), while also building ROS drivers/parameters for field-tunable behavior. Has hands-on experience integrating LegoLOAM and calibration/TF systems, including creating RViz visualization tools to validate transforms and debugging real-world drift issues caused by lighting/glare.”

“BI/analytics candidate with healthcare and product analytics experience spanning Honor Health and ASU. They’ve worked on messy multi-system hospital supply data and also owned analytics for an AI-powered tax assistant, with quantified outcomes including 97% faster search, 92% retrieval accuracy, 30% fewer ad hoc procurement requests, and 15% lower operational cost.”

“Early-career backend/data engineer with experience spanning university operations and banking systems. They’ve owned a Python-based automation that replaced manual alumni data maintenance at Georgia State University, cutting effort by 85% and centralizing 280+ profiles, and also worked on Java/Spring Boot services for legacy banking reconciliation and reusable multi-workflow APIs.”

“ROS2/Python robotics engineer who led a 4-person team building a simulated multi-robot warehouse system (SLAM + NAV2 + centralized task allocation) in Gazebo Ignition, including a distance/priority-based controller that reduced task completion time by ~30%. Also has hands-on real-time debugging/tuning experience for both mobile robots and a MyCobot 600 Pro manipulator, plus simulation work in CARLA using RL (TD3) and Social-LSTM for pedestrian behavior modeling.”

prashanth Jamalapurapu

Screened

Mid-level AI/ML Engineer specializing in data engineering, LLM/RAG pipelines, and recommender systems

5y exp

“Research assistant at St. Louis University who built and deployed a production document-intelligence RAG system (Python/TensorFlow, vector DB, FastAPI) on AWS, focusing on grounding to reduce hallucinations and latency optimization via caching/async/batching. Also developed a personalized recommendation system for the Frenzy social platform and partnered closely with product/UX to define metrics and iterate on hybrid recommenders and cold-start handling.”

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.”

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.”

keerthana bhukya

Screened

Junior Software Engineer specializing in AI/ML and cybersecurity

Stockton, CA2y exp

“Salesforce-focused engineer with hands-on depth across Sales Cloud, Service Cloud, Apex, LWC, and Aura. Stands out for owning end-to-end automation features, making thoughtful async architecture decisions to balance performance and reliability, and designing responsive Lightning interfaces that hold up under large data volumes.”

“Outbound-focused candidate with venture internship experience at Safar Partners, where they researched and mapped 40+ institutional investors and funds to support fundraising and partnership outreach. Stands out for combining structured prospecting, strong personalization, and data-driven iteration to generate warmer senior-level investor conversations.”

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.”

“Performance-focused paid social creative lead with deep Meta experience in DTC/eCommerce for a bridal brand, owning strategy, briefs, testing, and QA while delegating production. Known for rapid iteration (8–12 variants/week) and measurable lifts (2.8 ROAS; CPA down 25%+), plus strong UGC direction and landing-page alignment that materially improves conversion.”

“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/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).”

Sujay Surendranath Pookkattuparambil

Screened

Mid-level Machine Learning Engineer specializing in computer vision and reinforcement learning

Chicago, IL3y exp

“Early-stage engineer with hands-on embedded prototyping experience (Arduino/Raspberry Pi) who helped build an award-winning smart glasses project enabling phone notifications via Bluetooth. Strong computer vision performance optimization background, including accelerating 120 FPS inference by moving from TensorFlow to PyTorch and deploying through ONNX + TensorRT quantization, plus Docker-based GPU deployment and CI/ML practices.”

Srinivasan Gomadam Ramesh

Screened

Mid-level AI/Data Engineer specializing in agentic AI and data platforms

Redmond, WA7y exp

“AI/LLM engineer who built a production resume-parsing and candidate-matching platform at Quadrant Technologies, combining agentic LangChain workflows, VLM-based document template extraction (~85% accuracy), and a hybrid RAG backend for resume-to-JD search. Notably integrated automated LLM evals and metric-based CI/CD quality gates to catch silent prompt/model regressions, and led a 3-person team across frontend/backend/testing.”

“Robotics software engineer with ~4 years of ROS experience who implemented a real-time diffusion-policy control loop entirely in Gazebo, focusing on inference-latency reduction (warm-start + truncated denoising) for stable closed-loop execution. Has hands-on experience building custom ROS control nodes, optimizing AMR navigation (SLAM + RRT) with sensor-fusion for dynamic obstacles, and designing deterministic multi-robot coordination; also uses Dockerized ROS environments and automated simulation/benchmark pipelines.”