Vetted PyTorch Professionals

“Entry-level software engineer with strong full-stack experience building React/TypeScript and Node.js analytics products, especially around performance optimization for large datasets. Stands out for combining hands-on engineering with user discovery, and for delivering measurable wins like 40% fewer API calls, page load improvements from 3.2s to 1.1s, and 70% faster PostgreSQL queries during an internship at Tapastry.”

“Built and deployed LLM-powered agentic systems including a multi-agent travel planning assistant using LangChain, RAG (FAISS), real-time APIs, and a supervisor agent to manage coordination and reduce hallucinations. Also developed a Text-to-SQL system with schema-aware validation guardrails, and collaborated with drilling domain experts at CNPC USA to build an ML model predicting rate of penetration (ROP).”

“Full-stack product engineer who has built both operational systems and enterprise AI copilots in production. They owned an AI-powered inventory platform end-to-end, driving a 45% drop in stock issues, and also shipped a Microsoft Teams-based HR/IT copilot using RAG and workflow automation that reduced repetitive support queries by roughly 30%.”

“Built an internal AI-powered document search and Q&A platform at BNY that let employees query company documents in natural language and get grounded answers in seconds. Brings practical full-stack and LLM systems experience across React/TypeScript, FastAPI, Pinecone, OpenAI, and Claude, with clear emphasis on retrieval quality, hallucination reduction, and production monitoring.”

“Built and owned an AI-powered real-time financial fraud detection and monitoring platform end-to-end, spanning product decisions, backend architecture, frontend dashboards, deployment, and production support. Their work scaled to 120M transactions/day and materially improved fraud detection accuracy from 78% to 94%, showing rare breadth across distributed systems, observability, and React-based operational analytics.”

“Technical director and Unity engineer with 7+ years shipping products, including Symphoni, a mixed reality rhythm game launched across Meta Quest and Pico headsets. Particularly compelling for teams needing someone who can architect complex gameplay-adjacent systems end-to-end—such as a robust UGC level editor shipped on a 3-4 week timeline—while also bringing VR optimization, multiplayer networking, and emerging AI-assisted development workflow expertise.”

“Built across data, backend, analytics, and visualization-heavy applications, including a nonprofit financial forecasting app, large-scale insurance model analysis at Mercury Insurance, and a publicly deployed soccer analytics dashboard. Stands out for combining machine learning, large-dataset SQL work, and practical production improvements like cutting dashboard load times to under two seconds and refactoring codebases for smoother team handoff.”

Supreet Purthpli

Screened

Mid-level Software Engineer specializing in FinTech and cloud-native systems

San Francisco, CA4y exp

“Software engineer with JPMorgan Chase experience delivering end-to-end fintech features (Next.js/React/Node/Postgres on AWS) and measurable performance gains. Built and productionized an AI-native credit decisioning workflow combining LLMs, vector retrieval, and a rules engine with strong governance (bias checks, auditability, human-in-loop), improving precision and cutting underwriting turnaround time by 40%.”

“Robotics/AV engineer with strong ROS2 and autonomy stack integration experience, including bringing Autoware Universe up on a real Lexus autonomous vehicle platform. Also built a hierarchical reinforcement learning proof-of-concept for Boston Dynamics Spot (navigation + manipulation) and tackled sim-to-real challenges by implementing PD torque conversion for Jetson-based hardware; improved localization accuracy via GNSS+EKF fusion with a reported 28% drift reduction.”

“Built and deployed a production LLM-powered calorie-counting chatbot that turns plain-English meal descriptions into normalized food entities, quantities, and calorie estimates using a hybrid transformer + rule-engine pipeline. Emphasizes reliability with schema/constraint guardrails, confidence-based routing (including embedding similarity search fallbacks), and strong observability/metrics (hallucination rate, calibration, latency, cost). Partnered closely with nutritionists to encode domain standards into mappings and validation logic.”

“AI engineer who has shipped production LLM systems across customer service and marketing use cases—building a RAG app on Azure OpenAI and speeding retrieval with Redis caching tied to Okta sessions. Also implemented a LangGraph multi-agent workflow that pulls image context from Figma to generate structured HTML marketing emails, adding a verification agent to improve image-selection accuracy while optimizing solution cost for business stakeholders.”

“Robotics/ROS2-focused early-career engineer who built a stereo visual-odometry SLAM system for autonomous navigation and optimized it to run reliably in real time on Raspberry Pi. Strong in sensor fusion (camera+IMU), ROS2 debugging/profiling, and distributed robotics/IoT pipelines (ROS2 + MQTT + cloud), with added experience extracting WiFi CSI for sensing/localization and shipping via Docker + GitHub Actions CI/CD.”

“Built a web-based online Sudoku game in JavaScript (multiplayer format supporting up to 6 teams with up to 5 players each) and demonstrates strong product/analytics orientation. Uses a KPI-driven approach (DAU/WAU, ARPU, session duration, LTV) and structured prioritization methods (MoSCoW, story mapping, cost of delay, DFV) to iterate toward targets; seeking a remote role around $70k/year.”

“Master’s-in-Data-Science candidate (UHV) with 4+ years in AI engineering building production LLM and multimodal systems. Designed an LLM-powered workflow automation platform using RAG over vector stores with guardrails (schema/output validation, fallbacks) and a rigorous evaluation/monitoring framework including drift tracking and shadow deployments. Experienced orchestrating large-scale vision-language pipelines with Airflow and Kubernetes (OCR, distributed training) and partnering with non-technical ops stakeholders to cut cycle time and reduce errors.”

“Backend/platform engineer who owned a real-time customer analytics microservice stack in Python/FastAPI with Kafka streaming into PostgreSQL, including schema enforcement (Avro) and high-throughput optimizations. Strong Kubernetes + GitOps practitioner (EKS/GKE, Helm, Argo CD) who has handled CI/CD reliability issues with automated pre-deploy checks and rollbacks, and supported major migrations (on-prem to AWS; VM to EKS) with blue-green cutover planning.”

“React performance-focused engineer who contributed performance patches back to an open-source context+reducer state helper after profiling and fixing excessive re-renders in an enterprise project management platform at Easley Dunn Productions. Also built an end-to-end LLM-driven pipeline at Prime Healthcare to normalize millions of supply-chain records, reducing defects by 80% and saving 160+ hours/month.”

“Built a production, agentic multi-agent pharmaceutical intelligence system for US oncology (breast cancer) conference/news intelligence, automating MSL-style information gathering and summarization for pharma and healthcare stakeholders. Uses CrewAI + LangChain orchestration, custom scraping across ~15 pharma newsrooms, and a grounding-score evaluation approach (sentence transformers/cosine similarity) to mitigate hallucinations.”

“Designed and deployed an enterprise LLM-powered clinical/pharmacy policy knowledge assistant at CVS Health, replacing manual searches across PDFs/Word/SharePoint with a HIPAA-compliant RAG system. Built end-to-end ingestion and orchestration (Airflow + Azure ML/Data Lake + vector index) with PHI masking, versioned re-embedding, and production monitoring (Prometheus/Grafana), and partnered closely with clinicians/compliance to ensure policy-grounded, auditable answers.”

“Built and deployed a production LLM-powered clinical note summarization system used by care managers to speed review of 5–20 page unstructured medical records. Implemented safety-focused validation (prompt constraints, rule-based and section-level checks, human-in-the-loop) to reduce hallucinations while maintaining low latency and meeting privacy/regulatory constraints, integrating via APIs into existing clinical tools.”

“Robotics software engineer focused on ROS2 autonomy, with hands-on work building a monocular visual odometry system on KITTI (including GPS-based scale correction and RViz trajectory visualization) and an end-to-end Gazebo simulation integrating URDF, slam_toolbox, and Nav2. Demonstrates strong practical debugging skills around TF frames, lifecycle nodes, and Gazebo plugin/version compatibility.”

“AI/LLM engineer with production experience building secure, scalable compliance-focused generative AI systems (GPT-3/4, BERT) including RAG over internal regulatory document bases. Has delivered end-to-end pipelines on AWS with PySpark/Airflow/Kubernetes/FastAPI, emphasizing privacy controls, monitoring, and iterative evaluation (A/B testing). Also partnered closely with bank compliance officers using prototypes to refine NLP summarization/classification and reduce document review time.”

“Space/heliophysics ML engineer who built a PyTorch GRU model to propagate solar wind from L1 to the magnetopause with probabilistic outputs for uncertainty quantification, achieving ~25% better CRPS than standard approaches. Also developed production-grade Python ETL and an open-source telemetry processing package for a mission (LEXI), using Docker and GitHub Actions CI/CD and iterating with scientist/engineer stakeholders.”

“AI/LLM engineer with recent production experience at UnitedHealth Group building an end-to-end RAG system over structured EMR data and unstructured clinical notes, including evidence retrieval, GPT/LLaMA-based reasoning, and a validation layer for reliability. Strong in orchestration (Kubeflow/Airflow/MLflow), prompt engineering for noisy healthcare text, and rigorous evaluation/monitoring with gold-standard benchmarking, plus close collaboration with clinical operations stakeholders.”

“Robotics software engineer who built a lightweight, ROS-free distributed control and telemetry stack for a Caltrans long-range culvert inspection robot. Strong in integrating heterogeneous hardware (UART motor controllers, Ethernet sensors, MJPEG cameras) and delivering real-time operator data via FastAPI/WebSockets, including reverse-engineering undocumented protocols and debugging network-induced latency with control-loop redesign.”