Vetted PyTorch Professionals

“Robotics software candidate who designed and implemented a hierarchical motion-planning and whole-body control pipeline for a 37-state Spot robot to traverse complex terrain, using Graph of Convex Sets for safe footstep selection plus optimization-based IK and nonlinear trajectory optimization for joint trajectories/contact forces. Strong in optimization-heavy robotics workflows (PyDrake, MATLAB/Simulink) and methodical debugging down to signal-level and numerical stability; has not used ROS/ROS2 yet.”

“Robotics software engineer who built an open-source, real-time Cartesian controller for Universal Robots UR5/UR5e, targeting sub-mm accuracy at 500 Hz within ROS2/ros2_control. Demonstrates strong real-time debugging skills (timing profiling, singularity handling with Tikhonov regularization) and sim-to-real iteration using Gazebo/Isaac Sim plus physical hardware tuning; also has ROS1 experience building URDF/xacro and EKF configs for an underwater vehicle and has developed drone/robot packages.”

“Robotics software engineer who built a multi-agent dense warehouse mapping system in ROS 2, including LiDAR-camera fusion SLAM, timestamp-based synchronization, and DDS-based inter-robot pose/keyframe exchange under bandwidth constraints. Also applied Gaussian Splatting for selective photorealistic dense reconstruction and optimized real-time performance with node composition, bounded queues, and QoS tuning; experienced with Gazebo/CARLA/Unity simulation and Dockerized ROS 2 deployments.”

“Backend engineer who built a serverless AWS Lambda microservices backend for a parenting assistance mobile app, including a personalized recommendation system optimized to sub-500ms via precomputed scoring and DynamoDB caching. Demonstrates strong production pragmatism: CloudWatch-driven performance tuning (provisioned concurrency), zero-downtime phased schema migrations, and robustness patterns like optimistic locking and request deduplication. Also led a refactor of an LLM RAG pipeline to improve retrieval quality and cut latency from ~5s to ~3s.”

“Embedded C++ engineer with Bosch automotive infotainment experience, owning real-time audio middleware modules with strict latency/memory constraints. Strong in profiling/optimizing deterministic behavior, debugging hardware-specific intermittent issues, and building automated test + CI pipelines; currently ramping up on ROS2 concepts (DDS, nodes/topics/services) to transition toward robotics.”

“Built a production multi-agent recommendation/RAG system for internal data analysts to speed up weekly report creation by improving document discovery and automating report/SQL generation. Implemented LangGraph-based orchestration with deterministic agent routing, robust error handling (interrupt/resume), and metadata-driven semantic chunking for diverse PDF/document formats, plus monitoring for latency, throughput, and token/cost efficiency.”

“Developed and productionized an AI-driven "indication finding" system for AbbVie to identify additional diseases a drug could target, working closely with clinical research teams on cohort inclusion/exclusion criteria and disease rollups. Leveraged an LLM to map clinical inputs to ICD codes and built configuration-driven ML pipelines (Cloudera ML, YAML, scheduled jobs) with structured testing and evaluation for reliability.”

“Built and deployed a production LLM-powered multi-agent compliance copilot (life sciences/finance) using LangChain/LangGraph + RAG over vector databases, delivered via async FastAPI on Kubernetes. Emphasizes audit-ready, deterministic outputs with schema constraints and citations, plus rigorous evaluation/monitoring; reports 60%+ reduction in manual research time and successful production adoption.”

“LLM/agentic-systems engineer with PayPal experience hardening an LLM-powered fraud support assistant from prototype to production, focusing on low-latency distributed architecture, rigorous evaluation/testing, and security/compliance. Comfortable in customer-facing and GTM contexts—runs technical demos/workshops, builds tailored pilots, and aligns sales/CS with engineering to close deals and drive adoption.”

“Built and owned an end-to-end AV operations automation and dashboarding platform for USC event operations, used daily to coordinate hundreds of live events. Delivered a React/TypeScript full-stack system integrating Smartsheet APIs with strong reliability practices (typed contracts, validation/fallbacks, safe rollouts) and experience with queue-based microservice patterns (idempotency, retries, DLQs, monitoring).”

“LLM/agent workflow engineer with healthcare experience (CVS/CBS Health) who built and deployed a production call-insights platform using Azure OpenAI + LangChain/LangGraph, including sentiment and compliance checks. Demonstrates deep HIPAA/PHI handling (tenant-contained processing, redaction, RBAC/encryption/audit logging) and production rigor (testing, eval sets, validation/retries, autoscaling) to scale to thousands of transcripts.”

“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”

“Built and productionized a telecom-focused RAG assistant by LoRA fine-tuning LLaMA-2 and integrating LangChain+FAISS behind a FastAPI service, with dashboards and a human feedback UI for engineers. Demonstrated measurable impact (≈40% faster document lookup, +8–10% retrieval precision) and strong MLOps rigor via Airflow orchestration, CI/CD, and monitoring for drift and failures.”

“Built an LLM-powered learning assistant (EduQuizPro/EduCrest Pro) that uses RAG over URLs and PDFs to generate quizzes, notes, and explanations for students/professors. Emphasizes production robustness—implemented dependency fallbacks (FAISS/Sentence Transformers/Gradio), CLI-safe mode, and NumPy-based indexing—along with a custom orchestration layer to keep multi-step AI workflows reliable.”

“Building a production LLM/agent system for a leading US dental provider that extracts rules from payer handbooks/portals and EDI 271 responses to validate and improve patient cost estimates. Combines GCP stack (BigQuery, GKE, Cloud Run, Pub/Sub, Vertex AI) with strong agent reliability practices (observability, validator agents, grounding, PII/hallucination guardrails, confidence scoring) and has led non-technical customer stakeholders on enterprise ServiceNow↔Aha sync and AI-powered enterprise search/summarization.”

“Built and shipped a production NMT system translating medical documentation for a rare/low-resource language, tackling data scarcity with retrieval-driven pattern matching plus dictionary/grammar- and LLM-based augmentation and validating quality with a linguistic expert. Also develops agentic LLM workflows with LangChain/LangGraph (including a deep-research style system) and has experience aligning medical AI deployments with clinician-defined risk metrics and human-in-the-loop decision making.”

“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”

“Engineer with hands-on experience debugging and hardening a fixed-point implementation for an internal PoC, quickly diagnosing overflow/underflow issues that caused intermittent failures across thousands of runs and delivering a code fix. Comfortable presenting technical solutions with layered slide depth and doing follow-up deep dives for interested stakeholders, though has limited direct customer/sales partnership experience.”

“ML/NLP engineer currently working in NYC on a system that connects complex unstructured data sources to deliver personalized insights, using embeddings + vector DB retrieval and a RAG architecture (LangChain, Pinecone/OpenSearch). Strong focus on production constraints—especially low-latency retrieval—using FAISS/ANN, PCA, index partitioning, and Redis caching, plus PEFT fine-tuning (LoRA/QLoRA) and KPI/SLA-driven promotion to production.”

“Backend/full-stack engineer building complex internal platforms and customer-facing demos at the intersection of infrastructure and product. Shipped a no-code Product Lifecycle Manager for manufacturing (3 manufacturers, 1000+ evolving tests) using AWS S3/SQS ingestion and extensible Postgres (EAV+JSONB) with end-to-end traceability. Also built a FastAPI-based company data intelligence platform with Okta-secured RBAC and an LLM/MCP layer for ChatGPT-like analytics over enterprise data sources.”

“Built and shipped a real-time oncology risk prediction system used by doctors during patient visits, trained on clinical data in AWS SageMaker and deployed via FastAPI with sub-second responses. Emphasizes clinician-trust features (SHAP explainability, validation checks) and HIPAA-compliant controls (encryption, RBAC, audit logging), plus Kubernetes-based production operations with autoscaling, monitoring, and drift/retraining workflows; collaborated closely with oncologists at Flatiron Health.”

“Developed an LLM-driven recommendation agent in Azure Databricks to triage oncology patients and trigger second-opinion case creation using medical claims and EHR data. Uses ICD-10/CPT/J-code features in prompts, embeddings + vector DB similarity, and a backtesting framework emphasizing recall to avoid missing clinically relevant cases while supporting business revenue.”

“ML/NLP practitioner with experience building an embedding-based ad matching and search system at Vericast (BERT embeddings + similarity search) to replace a third-party taxonomy approach, evaluated via a human-curated gold standard. Also built a custom NER pipeline at Allstate for auto accident claims calls using a bidirectional LSTM and achieved 90%+ F1, with a strong emphasis on production-grade ML workflows (testing, CI/CD, orchestration, versioning, validation).”

“Built a production RAG-based GenAI copilot backend at Aetna using Python/FastAPI, GPT-4, LangChain, and Azure AI Search, deployed on AKS with Prometheus/Grafana observability. Owned the system end-to-end (ingestion through deployment) and improved peak-time reliability by addressing vector search and embedding bottlenecks with Redis caching, index optimization, and async processing, plus added anti-hallucination guardrails via retrieval confidence thresholds.”