Vetted PyTorch Professionals

“Built end-to-end AI features across both fitness and insurance domains, including a full-stack personalized workout recommendation system and a production RAG-based insurance QA assistant at Relevance Labs. Stands out for combining backend/distributed systems skills with practical LLM architecture, evaluation, and risk-aware human-in-the-loop design; notably reduced unnecessary LLM calls by 40% while improving latency and answer reliability.”

Amre Amer

Screened

Senior Unity XR Engineer specializing in mixed reality, ML, and robotics

Reno, NV14y exp

“Unity/C# developer with unusually broad hands-on experience spanning gameplay systems, VR/Quest hand-tracking ML, low-latency LAN multiplayer, and AR enterprise training tools built from CAD data. They’ve delivered measurable performance wins like 10x smaller animation files and 72Hz runtime control, and have owned end-to-end technical features from data pipeline design through stakeholder buy-in and shipped prototypes.”

“Backend engineer with hands-on experience building Flask REST APIs (auth, CRUD, S3 media uploads) and driving measurable Postgres/SQLAlchemy performance gains (p95 reduced to 200–400ms by eliminating N+1s and switching to keyset pagination). Implemented multi-tenant isolation with strict tenant scoping plus Postgres RLS, and built an OpenAI-powered quiz generation pipeline using queued workers, structured JSON outputs, and Celery/Redis optimizations to stabilize high-throughput workloads.”

“Robotics software candidate focused on simulation-to-learning workflows, building a novel-view-synthesis pipeline (USCiLab3D) with a multi-modal diffusion model and a LiDAR-driven, geometry-aware sampling strategy for selecting overlapping reference views across trajectories/seasons. Also designed coordinated motion planning for two Ridgeback-Franka robots in Isaac Lab for a non-prehensile collaborative task, augmenting controller limitations with RL-based self-collision termination states.”

“CMU candidate with hands-on experience taking LLM concepts from research prototypes toward production-ready designs (structured outputs, guardrails, failure-scenario evaluation). Also partnered with sales/customer teams at Mazecare to drive adoption with Dontia Alliance (largest dental clinic chain in Singapore) and engaged Singapore government stakeholders, bridging clinical workflow needs with IT security/integration concerns.”

“Robotics software engineer with Samsung Research America internship experience as primary developer on a real-time dense mapping system producing point clouds, plus a monocular depth-estimation framework using positional data. Hands-on ROS 2 and CAN integration from a University of Michigan autonomous shuttle project, and practical SLAM/motion-planning experience including handling the kidnapped robot problem and Dockerizing ORB-SLAM3 environments.”

“Built and deployed an on-prem, HIPAA-compliant LLM pipeline for oncology-focused clinical note generation and decision support, emphasizing grounded differential diagnosis and explainable reasoning via RAG to reduce hallucinations. Also created a LangGraph-based multi-agent academic paper search system integrating Tavily, arXiv, and Semantic Scholar with an orchestrator that routes tasks to specialized sub-agents.”

“Built a production LLM-powered RAG agent for healthcare/insurance operations that retrieves and summarizes patient medical documents with grounded citations, scaling to ~4.5M records. Addressed medical shorthand and terminology by fine-tuning ~120 lightweight DistilBERT models by specialty and validating entities against SNOMED/RxNorm, while using SHAP/LIME and human-in-the-loop review to make decisions explainable to stakeholders.”

“AI engineer with production experience building a high-accuracy (98%) fraud detection system operating at real-time latency (1–2s) over millions of transactions, using a multi-model pipeline approach to meet performance constraints. Also implemented Airflow-orchestrated workflows (DAGs, retries, alerts) to replace brittle cron scripts and is currently pursuing a master’s project on real-time ASL-to-text conversion.”

“Full-stack AI/LLM engineer who has deployed a production LLM backend (Mistral 14B) on GKE to auto-transform datasets and generate runnable ML training pipelines, addressing hallucinations, schema mismatch, latency, and burst scaling with caching/prompt compression and HPA. Also has internship experience (Splunk, BlackOffer) delivering data automation and 10+ Power BI dashboards for non-technical stakeholders with measurable efficiency gains.”

“Robotics engineer who built a 6-axis force-torque sensor system end-to-end at ROAM Lab, including electronics, low-level drivers, and ROS2 live inference with time-series deep learning (ultimately a 1D ResNet) to handle highly noisy, session-shifting signals. Also upgraded tactile manipulation models to time-series inputs by modifying long-standing ROS architectures, and has prior experience in defense (L3Harris) with production-grade testing and code review practices; published work: arxiv.org/abs/2410.03481.”

“Robotics software engineer with hands-on experience building a dual-arm (Kawasaki duAro) Cranfield assembly task-planning and motion-planning stack in ROS/MoveIt, using PDDL + behavior trees and OMPL for collision-free execution. Improved tight-tolerance insertions by integrating RGB-D visual servoing into the task planner loop, and also built an LLM-driven navigation pipeline with ORBSLAM3 for natural-language command parsing and real-time replanning.”

Vamshikrishna Bandi

Screened

Senior AI/ML Engineer specializing in Generative AI and agentic multi-agent systems

6y exp

“Built and shipped a production LLM-powered multi-agent RAG system to automate complex internal support workflows, integrating tool execution (SQL/APIs) with validation guardrails to reduce hallucinations. Optimized for real-world latency and cost via model routing, caching, and async parallel tool calls, and enforced reliability with CI-gated golden test sets derived from anonymized production queries.”

Praveen Nutulapati

Screened

Mid-level Generative AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems

New York, NY6y exp

“Built and deployed a production multi-agent RAG system at JPMorgan Chase to automate regulated credit analysis and compliance clause discovery across large internal policy/document libraries. Implemented LangGraph-based supervisor orchestration with structured state management (Azure OpenAI) to support long-running, resumable workflows, plus hybrid retrieval + re-ranking and guardrails for reliability. Strong at evaluation/observability (trace logging, LLM-judge, HITL) and at communicating results to non-technical stakeholders via Power BI embeds and Streamlit prototypes.”

Vedant Kharwal

Screened

Intern AI/ML Engineer specializing in Generative AI and applied machine learning

Mumbai, India1y exp

“New graduate with hands-on LLM work building a RAG pipeline (HNSW, lexical reranking/boosting, ReAct) and optimizing it through ablation to dramatically reduce latency. Also building a modular personal assistant with a custom wake word model, router-driven agent selection, and integrations like Spotify with secrets managed via .env.”

Chancellor Day

Screened

Intern Robotics/Software Engineer specializing in autonomy, computer vision, and controls

San Diego, CA0y exp

“Robotics software engineer with a master’s focused in the field who has integrated a multi-sensor robotics fusion laser system (fault detection, PLC comms, PyTorch-based CV diagnostics, and an engineer-facing status front end) under NDA. Has ROS experience from the University of Michigan Autonomous Robotic Vehicle team using Nav2/SLAM Toolbox/Gmapping with RViz and ROS bag-driven debugging, plus Gazebo simulation work and upcoming drone path-planning optimization research.”

Venu Venkata Surendra reddy Erusu

Screened

Mid-Level Software Engineer specializing in AI/ML and Cloud-Native Microservices

Syracuse, NY4y exp

“Research assistant at Syracuse University who owned a Python/FastAPI analytics backend for user-uploaded large datasets, using S3 streaming uploads and background workers for heavy processing. Has hands-on experience deploying Dockerized Python/Java microservices to AWS EKS with Jenkins-based CI/CD, plus Kafka-based event-driven pipelines and practical migration patterns (dependency mapping, dual-write, reconciliation) to minimize downtime.”

Vasudha Prerepa

Screened

Mid-Level Java Full-Stack Developer specializing in cloud-native microservices

5y exp

“QA/validation-focused engineer with experience at Meta testing an ML+LLM content classification/summarization system, including production-vs-test behavior gaps. Built automated E2E validation and drift monitoring (PSI, KL divergence, embedding cosine similarity) run daily/multiple times per day and gated via CI. Also implemented Jenkins-orchestrated Selenium/API test suites in Docker at Capgemini and partnered with a business analyst to convert business rules into automated AI-driven validation checks.”

“End-to-end deployment owner for Oracle document delivery/print services in a hospital-like production environment, focused on reliability/performance at scale (thousands of systems). Also describes implementing event-driven RAG/agentic LLM workflows with attention to embeddings/index consistency, latency, and measurable improvements in response relevance and operational efficiency.”

“Built Baioniq, an enterprise LLM platform for automating extraction from massive unstructured documents like contracts and insurance claims. They demonstrate unusually strong production depth in agentic AI—scaling to 100k+ requests/day, processing 1M+ claim documents, and improving extraction accuracy through rigorous RAG architecture, evaluation, and fallback design.”

“Candidate combines fraud analytics experience at Citi with a clinical AI capstone involving reproducible ML pipelines for imaging and notes data. They stand out for turning messy, high-volume data into decision-ready reporting, automating evaluation workflows, and translating analytics into operational impact—from fraud rule changes to retention metric adoption.”

Sirisha Maddikunta

Screened

Mid-level Generative AI Engineer specializing in enterprise LLM and healthcare AI solutions

O Fallon, MO6y exp

“Built and owned an end-to-end LLM-powered fraud investigation assistant that automated case summaries and risk analysis, cutting analyst investigation/documentation time by 40%. Stands out for translating RAG concepts into a production-grade internal platform with strong evaluation, monitoring, and reusable Python service architecture that improved both analyst trust and engineering velocity.”

“Built and owned a production conversational AI platform for a healthcare contact center, including RAG-based agent assist, hybrid retrieval, safety guardrails, and production monitoring. Stands out for combining LLM product delivery with strong operational rigor, driving a reported 25-30% improvement in handling time in a sensitive healthcare environment.”