Vetted PyTorch Professionals

Ziyu Wang

Screened

Mid-level Full-Stack Engineer specializing in AI platforms and agents

New York, NY4y exp

“Front-end engineer focused on high-performance real-time browser applications, with hands-on ownership of an AI-integrated education platform and interactive admin tooling. Stands out for deep browser fundamentals knowledge and measurable UI performance gains, including a 40% reduction in client-side response latency.”

“Robotics software engineer with hands-on experience across reinforcement learning and ROS/ROS2, including a project teaching Boston Dynamics Spot to open a door by combining vision-based pose estimation with SAC-trained IK and a walking policy in MuJoCo. Previously built ROS Noetic control for surgical robots using RCM with MoveIt IK and achieved sub-0.02s latency via threading; also participated in a NASA ROS2 space simulation building rover teleop and sensor-driven mapping.”

“Robotics software engineer who owned the end-to-end navigation stack for a mobile manipulation robot (Cone-E), integrating ZED-2i SLAM into a real-time occupancy grid with live obstacle avoidance, A* planning, and lookahead control. Strong in real-time debugging and stability improvements (goal snapping/locking, obstacle persistence, rate-limited replanning) and validates changes on hardware, supported by simulation (Gazebo/Webots) and Docker/CI-based testing.”

“Early-career robotics engineer who has integrated LLM/NLP command interfaces (OpenAI/LLaMA) into ROS-controlled industrial manipulators and built data-driven controls for underwater soft robotic actuators. Combines hands-on fabrication (balloon actuator with embedded copper traces) with sensor debugging (IMU/Aurora) and simulation work in Gazebo, with practical exposure to edge deployment constraints on Jetson Nano and model quantization.”

“LLM/RAG practitioner who productionized an LLM-driven customer communication and transaction understanding system at PayPal, emphasizing privacy/compliance guardrails and large-scale data normalization. Experienced in real-time debugging of hallucinations via retrieval pipeline tuning and in leading hands-on developer workshops and sales-aligned POCs to drive adoption.”

“Computer-vision-focused candidate with internship experience at ASML (Silicon Valley) building object detection models (YOLO, RT-DETR) for SEM defect inspection. Worked end-to-end on preparing multi-resolution datasets and tuning/training strategies, noting improved performance on low-quality images when training jointly on higher-resolution data.”

“Built a production AI-driven QA automation platform using a multi-agent architecture (MCPs + LangGraph) to run parallel website tests across multiple device environments via automated image building and containerization. Currently collaborating with restaurant operators and managers to deliver an agentic restaurant analytics system, emphasizing deep domain discovery with non-technical stakeholders.”

“ML/NLP engineer with experience at Flatiron Health building a production NLP platform that processed millions of clinical notes, using BERT/BiLSTM-CRF and spaCy to extract and normalize entities from noisy EMR text with oncologist-in-the-loop validation. Also built scalable retail ML workflows (Spark + Kubernetes + feature store caching) and applied vector databases plus contrastive-learning fine-tuning to improve retrieval relevance and recommendations.”

“Backend/DevOps-focused engineer with healthcare and financial systems experience, including an ICU readmission risk platform delivering real-time ML scores via a secure FastAPI service (PyTorch model serving, PostgreSQL, Celery/Redis) deployed on AWS with strong observability. Has hands-on Kubernetes GitOps delivery (Helm, ArgoCD, HPA) and has supported a JPMC on-prem-to-AWS microservices migration using phased validation and blue-green cutovers, plus Kafka/Avro streaming for real-time transaction processing.”

“Real-time 3D/C++ developer with hands-on engine-level systems work, including a 3D positional audio/Doppler pipeline stabilized against frame-rate jitter via fixed-timestep + interpolation architecture. Built a runnable 3D engine project featuring custom collision detection/response (AABB, SAT, sphere) with unit and edge-case testing, and has UE5 multiplayer movement experience implementing a custom sprint mode using Character Movement (SavedMove, intent prediction).”

“Completed an Amazon externship building a GPT-4 + RAG pipeline to summarize themes from hundreds of employee reviews for workforce analytics aimed at improving warehouse retention. Emphasizes production-readiness through labeled-data evaluation, source attribution for explainability, human-in-the-loop review, and rigorous data cleaning/observability to debug real-world LLM workflow issues.”

“Software engineer with strong end-to-end ownership across frontend, backend, data, and infrastructure, including real-time systems (Kafka/Postgres) and observability (Datadog). Built and productionized an AI-native RAG support assistant (OpenAI embeddings + Pinecone) with prompt/guardrail design, achieving 48% agent adoption and 30% faster responses. Experienced in legacy modernization and reliability work using feature flags, event/transaction replay, and rapid embedded delivery.”

“Built and productionized an internal LLM-powered document Q&A system at Morgan Stanley using a LangChain-based RAG pipeline (FAISS + OpenAI) with AWS ingestion (S3/Lambda), handling 100k+ pages and cutting lookup time ~35% while keeping responses under 3 seconds. Strong on reliability: automated evals/CI (pytest + GitHub Actions), CloudWatch monitoring, drift detection (prompt drift and fraud-model drift), and security controls (IAM + app-level authorization) in a financial-services environment.”

Yasser Ali

Screened

Junior AI & ML Engineer specializing in agentic systems and full-stack AI products

San Francisco, CA2y exp

“Won a machine learning contest and was placed onto a Kaiser data science team, where they built ML models for hospital bottleneck prediction and resource allocation. They later built and deployed a full-stack LLM-based “data analyst agent” (with custom orchestration plus LangChain/OpenAI Agents experience) that generates analysis code, answers questions, and produces dashboards from uploaded datasets, emphasizing rigorous evaluation sets, robustness, and healthcare security/compliance integration.”

Pavan Kumar Malasani

Screened

Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and GenAI

Remote, USA4y exp

“GenAI/ML engineer in Citigroup’s finance environment who has deployed production RAG systems for investment banking under strict privacy and model-risk constraints. Built an internal-VPC Llama2 + Pinecone + LangChain solution with NER redaction and citation-based verification to prevent hallucinations, delivering major time savings, and also partnered with global finance executives to ship an AI early-warning indicator for treasury/liquidity risk.”

Dinesh Kumar Patibandla

Screened

Mid-level Machine Learning Engineer specializing in LLMs and RAG for finance and healthcare

Texas, USA4y exp

“ML Engineer with recent Goldman Sachs experience building and deploying a production RAG/LLM assistant for summarization, drafting, and internal knowledge retrieval across financial, risk, and compliance documents. Designed for heavy regulatory constraints and scaled to 10,000+ concurrent users using Kubernetes-based orchestration, dynamic LLM routing, and rigorous testing (adversarial prompts, A/B tests, load simulations) with privacy controls like differential privacy.”

Sandeep Athota

Screened

Mid-level AI/ML Engineer specializing in cloud MLOps and production ML systems

Texas, USA4y exp

“AI/ML engineer at J.P. Morgan Chase who deployed a production financial-risk prediction platform combining CNN/LSTM/gradient boosting on AWS SageMaker, with automated drift-triggered retraining and governance-grade fairness testing. Leveraged SageMaker Clarify plus SMOTE and LLM-generated synthetic data to improve minority-group F1 by 0.12, and communicated results to non-technical risk/ops teams via Power BI dashboards.”

Abhay Ravi Kumar

Screened

Junior Software Engineer specializing in backend, cloud DevOps, and ML/NLP

Bengaluru, India1y exp

“DevOps/data-automation professional with HPE experience who has deployed containerized microservices to AWS EKS and built an end-to-end observability stack (Prometheus/Grafana/CloudWatch via Terraform), reporting zero-downtime deployments and ~40% faster incident response. Also extends Python ETL automation for procurement/operations teams (rules engine, validation, performance tuning) and bridges SAP ERP data into Power BI/Qlik dashboards through close on-site user collaboration.”

Pandari G

Screened

Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems

San Francisco, USA5y exp

“GenAI/LLM engineer with production deployments in both fintech and retail: built an AI-powered mortgage document analysis/automated underwriting pipeline at Fannie Mae (OCR + custom LLM) cutting underwriting review from 3–4 hours to under an hour with privacy-by-design controls. Also helped build Sephora’s GenAI product advisory bot using LangChain-orchestrated RAG (Azure GPT-4, Azure AI Search, MySQL HeatWave vector search), focusing on grounding, evaluation, and compliance-aware architecture choices.”

Yukti Kamthan

Screened

Senior Software Engineer specializing in AI/ML and data systems

Mumbai, India10y exp

“Built and shipped production LLM/AI agent systems including an NL-to-SQL query agent with semantic search and Redis-based caching, using schema-aware prompting and threshold validation to reduce hallucinations. Has orchestration experience running ML microservices on Kubernetes and automating event-driven insurance (P&C) workflows (claims/policy + fraud checks), reporting ~60% manual overhead reduction and ~99% uptime, with strong monitoring/drift-detection and business-facing Power BI reporting.”

HEMANTH KUMAR KOTTAPALLI

Screened

Mid-level Machine Learning Engineer specializing in GPU-accelerated LLMs and MLOps

GA, USA4y exp

“Built and deployed a production LLM-powered decision-support system for supply-chain planners that explains demand forecast changes using grounded retrieval from sales, promotion, inventory, and supplier data. Implemented strict anti-hallucination guardrails and latency optimizations, deployed as a real-time AWS API with monitoring, and reported ~15% forecast accuracy improvement and ~12% supply-chain risk reduction. Experienced orchestrating data/ML/LLM workflows with Airflow, LangChain/LangGraph-style patterns, and AWS Step Functions while partnering closely with non-technical business users via demos and example-based requirements.”

Anishkumar Mahalingam Iyer

Screened

Intern Software Engineer specializing in AI/ML infrastructure and applied machine learning

Palo Alto, CA2y exp

“Interned at Rivian where they built and deployed a production Whisper-based ASR + LLM real-time event labeling pipeline to help autonomous-vehicle engineers diagnose failures and route issues to triage teams. Also built a stateful multi-agent "Code Partner" developer assistant using LangGraph/LangChain (planner/router/coder/critique/tester) with evaluation, adversarial testing, and stakeholder-friendly communication practices.”

Ahmed Atallah

Screened

Senior Controls & Localization Engineer specializing in autonomy, sensor fusion, and MPC

Troy, Michigan12y exp

“Robotics software engineer focused on state estimation and localization reliability, with deep hands-on EKF tuning/validation using DGPS ground truth and integrity-risk-based uncertainty calibration. Built middleware-agnostic interfaces with ROS wrappers to enable repeatable ROS bag playback testing, and implemented CI at Caterpillar to automatically build the localization stack and run unit tests plus bag-based regressions before merge.”

Atyab Hakeem

Screened

Junior Data Scientist / ML Engineer specializing in GenAI and computer vision

San Francisco, CA2y exp

“Software engineer who built and deployed OddPulse, a multi-agent LLM-powered continuous financial auditing system aimed at reducing compliance penalties by catching issues before audit cycles. Experienced with TrueAI-based agent orchestration, Airflow on GCP batch workflows, and rigorous evaluation/benchmarking (hit rate/MRR, latency/TTFT, cost) alongside security controls for sensitive financial data.”