Vetted Research Assistants

“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.”

“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.”

“Senior Unity game programmer who shipped a Meta Quest VR game (Unearthed VR) and owned key systems spanning gameplay, UI, shaders/VFX, and VR interaction. Notably built a reusable, general VR interaction framework to make interactions intuitive and user-friendly, and emphasizes scalable, data-oriented architecture for maintainable Unity projects.”

“At ReferU.AI, designed and deployed an agentic RAG pipeline that automates multi-jurisdiction legal document drafting, emphasizing hallucination reduction through hybrid retrieval, validation agents, guardrails, and iterative regeneration. Experienced with orchestration frameworks (especially CrewAI) and rigorous testing/evaluation practices including human-in-the-loop review, adversarial testing, and production metrics/logging.”

“Robotics/ML engineer who benchmarks and adapts open-source robot action models, building synthetic datasets in Isaac Sim and modifying vendor code to scale training across multiple GPUs. Also built a production-style computer vision pipeline at Zortag—training a tiny YOLO-based classifier for fake-vs-real label detection and deploying it in a real-time iOS app with additional display/spoof detection.”

“Early-career robotics software engineer with a couple years of ROS/ROS2 experience focused on agricultural mobile robots. Led integration of a Livox MID-360 3D LiDAR on the Farm-ng Amiga platform, patching ROS2 drivers/QoS and building a 3D-to-2D mapping pipeline so Nav2 could run reliable SLAM/navigation in GPS-denied greenhouse/hop-field environments, enabling stable autonomous row-following.”

“UE5 gameplay scripter (Blueprint-focused) from small startup teams who specializes in setting up core project architecture (game loop/data flow, controller, game instance, UI/menu) and building modular systems reused across multiple games. Rebuilt a save-system architecture from scratch to replace a problematic asset and improve scalability/maintainability, and has hands-on performance optimization experience (profiling, level streaming, Nanite-to-LOD tradeoffs). Also implemented a realistic cricket match simulation using Markov chains and transition matrices.”

Ashay Panchal

Screened

Mid-level Software Engineer specializing in AI-driven distributed systems

San Jose, CA4y exp

“Backend engineer who built a high-stakes, privacy-first platform at be Still Analytics for survivors of domestic violence, emphasizing anonymity, security, and reliability. Experienced with GenAI backends (LangChain + AWS Bedrock) including RAG to prevent hallucinations, plus cloud-native scaling (Docker/Kubernetes) and cost-saving migrations from legacy VMs to serverless (30% reduction).”

“Senior engineer with experience at Micro Focus/HPE (now OpenText) supporting enterprise customers on monitoring platform deployments, upgrades, and ongoing product use. Brings hands-on expertise in operations agents, agentless integrations, security-related upgrade troubleshooting, and cross-functional escalation with engineering to keep customer environments stable and successful.”

“Backend-focused engineer with experience at Ramp, Easebuzz, and George Mason University, spanning data pipelines, workflow automation, and production reliability. Stands out for quantifiable performance gains, strong debugging instincts in distributed job systems, and translating ambiguous finance operations processes into measurable automation outcomes.”

“Robotics software engineer working on GNSS-denied UAV localization using 5G PRS (OpenAirInterface + USRP B210) and multi-sensor fusion, with a published AIAA SciTech 2026 result achieving 1.5m RMSE on low-cost hardware. Also integrating a Vision-Language-Action model (SmolVLA) onto the Stretch 2 platform for language-assisted manipulation, leveraging ROS 2, imitation learning data collection with RGB-D, and simulation in Gazebo/MuJoCo for sim-to-real deployment.”

“Built a production Apple-focused LLM Q&A bot that answers user issues using similar past discussion records, including large-scale scraping and cleaning of thousands of forum threads. Used BeautifulSoup + Playwright for static/dynamic extraction, PySpark + NLP for preprocessing, and LangChain RAG with a custom response-likeliness metric to evaluate performance.”

Krishna K

Screened

Junior Machine Learning Engineer specializing in multimodal systems and LLMs

Jersey City, NJ2y exp

“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”

Mohammed Syed

Screened

Mid-level AI Engineer & Researcher specializing in healthcare AI and multimodal LLM systems

Remote2y exp

“Backend/ML engineer focused on clinical AI transparency who built ShifaMind, an explainability-enforced clinical ML system using UMLS/MIMIC-IV/PubMed data with RAG, GraphSAGE, and cross-attention. Demonstrated strong production engineering via FastAPI API design and safe migrations (feature flags/shadow inference), plus HIPAA-aligned auth/RLS patterns; also delivered a real-time comet detection system reaching 97.7% accuracy.”

Sai Harsha Kurapati

Screened

Mid-level Backend Engineer specializing in distributed systems and industrial IoT

Indianapolis, IN4y exp

“Backend/Python engineer focused on real-time sensor/IoT analytics: built dashboards and a high-throughput ingestion pipeline (MQTT -> Python worker -> TimescaleDB) with buffering, batch inserts, and validation. Strong Kubernetes + GitOps practitioner (Dockerized microservices, HPA, probes, ArgoCD) who has handled production incidents like CrashLoopBackOff under peak load and supported an on-prem analytics migration to AWS using shadow traffic and rollback plans.”

TiffanyUtvaer Gasser

Screened

Mid-Level Software Developer specializing in digital banking

Foster City, CA4y exp

“Frontend-leaning engineer with strong React/Next.js (App Router + TypeScript) experience who has handled complex migrations (Pages to App Router) and performance-focused SSR work in a real estate inventory product. Also built regulated banking workflows (credit card disputes) using XState for correctness and implemented BullMQ/Redis queueing plus observability dashboards; background spans banking, academia, and NGOs, emphasizing scrappy delivery under constraints.”

Matthew Affinito

Screened

Entry-Level Entrepreneurship & Product Strategy Candidate in EdTech and Innovation

San Luis Obispo, CA

“Founder who spent a year building an EdTech startup (Hopscotch) and developed high-volume outbound sourcing skills from zero-intro outreach across LinkedIn, cold email, and communities. Demonstrates strong EdTech GTM understanding (school district purchasing) and uses transparent, research-driven relationship building to turn cold outreach into high-quality founder/investor conversations that influence product direction.”