Vetted Deep Learning Professionals

“Built and shipped a production LLM/RAG risk-case summarization and triage system used by fraud/compliance analysts, with strong grounding controls (evidence-cited outputs and refusal on low confidence). Demonstrates end-to-end ownership across retrieval quality, Airflow-orchestrated indexing pipelines, and compliance-grade privacy (PII redaction, RBAC, encrypted redacted logging, and auditable prompt/model versioning) plus a tight feedback loop with non-technical domain experts.”

“GenAI/ML engineer from Lucid Motors who built and productionized an LLM-powered RAG diagnostic assistant for manufacturing and maintenance teams, deployed on AWS with Docker/Kubernetes and MLflow. Demonstrates end-to-end ownership from retrieval/prompt design to scalability, monitoring, and workflow integration via APIs, plus production ML pipeline orchestration with Kubeflow (Spark/Kafka + TensorFlow) for predictive maintenance use cases.”

“Backend engineer at Goldman Sachs who deployed internal LLM-powered utilities to summarize operational logs/tickets, with a strong emphasis on data sensitivity and reliability. Built deterministic workflows with template-based prompts, confidence checks, and rule-based fallbacks, and used monitoring plus failure-rate metrics to tune performance; also has hands-on Temporal orchestration experience for resilient async backend jobs.”

“Built and deployed a production LLM-powered RAG assistant for semiconductor manufacturing failure analysis, reducing engineer triage effort by grounding outputs in retrieved evidence and gating responses with SPC + ML signals (LSTM anomaly scores, XGBoost probabilities). Experienced with LangChain/LangGraph to ship reliable, observable multi-step agents with branching/fallback logic, and evaluates impact using both technical metrics and business KPIs like mean time to triage and downtime reduction.”

“Built and deployed a production LLM/RAG chatbot at Wells Fargo for securely querying regulated financial and compliance documents, emphasizing low hallucination rates, explainability, and strict governance. Experienced with LangChain multi-agent orchestration plus Airflow/Prefect pipelines for ingestion, embeddings, evaluation, and retraining, and partnered closely with compliance/operations to drive adoption through demos and feedback-driven retrieval rules.”

“Internship experience building production-oriented AI systems, including a real-time voice scam/spoof detector (RawNet + AASIST) hardened for noisy audio via aggressive augmentation and Zoom-based noise simulation, evaluated with EER on clean and wild datasets. Also built an LLM-driven UI automation agent using Playwright for apps like Linear/Notion with modular tool design, unit tests, and replayable scripted scenarios, and has AWS Step Functions experience orchestrating Lambda/Cognito workflows.”

“Founder/builder of an EdTech startup (robograde.io) who personally conducted on-site classroom discovery with teachers and rapidly iterated the product based on real-world feedback. Implemented a Canvas LMS integration and refined it through weeks of in-person testing, and handled a live production grading failure by quickly debugging and deploying a fix, then adding fault-tolerant/backup API design.”

“AI engineer with robotics perception experience at Forterra, building and deploying moving-object/obstacle detection models into real-time robot pipelines. Addressed training crashes/latency via sub-batch training and optimizer tuning, and improved debugging using ROS/ROS2 tooling with 3D voxel visualization and color-coded validation.”

“Data/ML practitioner with hands-on experience building NLP systems from prototype to production: delivered a Twitter sentiment classifier with robust preprocessing, SVM modeling, and Power BI reporting, and built entity-resolution pipelines for messy multi-source customer data (reporting ~95% improvement in unique entity identification). Also implemented semantic linking/search using SBERT embeddings with FAISS vector retrieval and domain fine-tuning (reported ~15% precision lift), and applies production workflow best practices (Airflow/Prefect, Docker, Azure ML/Databricks, Great Expectations).”

“Built and deployed a production LLM-powered RAG document intelligence/Q&A system for healthcare prior authorization, reducing manual medical document review time and improving decision efficiency. Strong in end-to-end LLM application engineering (LangChain/LangGraph), retrieval quality improvements (hybrid search, embedding tuning, chunking strategies), and rigorous evaluation/monitoring for reliability.”

Nikhil Soni

Screened

Junior AI/ML Engineer specializing in LLM systems and retrieval-augmented generation

New York, NY2y exp

“Built and deployed a production LLM-powered market intelligence and decision-support platform for noisy, real-time financial data, using a high-throughput embedding + vector DB RAG architecture to reduce hallucinations while keeping latency and cost low. Operated it at scale with GPU-backed inference (continuous batching/quantization), FastAPI on Kubernetes, and Airflow-orchestrated ingestion/embedding/retraining workflows, with strong schema-based reliability and monitoring.”

Rui Cheng

Screened

Mid-level Software Engineer specializing in autonomous driving simulation and 3D mapping

5y exp

“Founding software engineer who built an autonomous-vehicle 3D digital twin using Unreal Engine 5 and CARLA, owning core simulator logic (traffic/scenarios/weather) and a ROS 2-based pipeline to record synchronized multi-sensor data (RGB/depth/segmentation/LiDAR/IMU/GPS). Also implemented distributed synchronization patterns (server + client prediction) using FastAPI and WebSockets; seeking roles with H1B transfer and targeting ~$110k.”

Yunxi Zhu

Screened

Entry-level Robotics & Automation Engineer specializing in robot learning and manufacturing automation

Houston, TX1y exp

“Robotics software engineer focused on real-time teleoperation and high-quality robot-learning data pipelines, including synchronized multimodal sensing (RGB-D, tactile, joint states) for Diffusion Policy training on a bimanual ALOHA robot. Strong ROS practitioner who debugs real-time control issues with ROS tooling and builds simulation environments in Isaac Lab and PyBullet; also packages data-collection stacks with Docker.”

Rajesh Nagula

Screened

Mid-level Robotics Software Engineer specializing in real-time control and perception

Manchester, NH4y exp

“Robotics software engineer focused on controls and motion planning for autonomous flight systems using ROS 2 (rclcpp), Gazebo/RViz, and BehaviorTree.CPP. Has hands-on real-time control experience (1ms loop rate) and has improved system performance by tracing latency issues and refactoring vision components (singleton camera init). Also built low-latency Ethernet/TCP comms on top of the IgH Ethernet stack and uses digital-twin simulation (Gazebo, MuJoCo; beginner Isaac Sim) to validate algorithms.”

Ishaan Nanal

Screened

Intern-level Software Engineer specializing in backend systems and AI/ML

Ithaca, NY1y exp

“Built and shipped an LLM-powered RAG research copilot used by 20+ users across biology, physics, and ML, cutting literature review from days to minutes. Strong focus on production reliability—iterated on chunking/retrieval/prompting, added validation and modular pipelines for debuggability, and is now containerizing and scaling the system with Docker and GCP.”

Harrishkumar Loganathan

Screened

Mid AI/Machine Learning Engineer specializing in FinTech and Generative AI

Remote, USA3y exp

“AI/ML engineer with hands-on ownership of enterprise LLM deployments at Freshworks, including a large-scale RAG chatbot serving 15,000+ users across six departments. Stands out for combining deep production engineering skills—AWS microservices, Kubernetes, observability, retrieval quality, and faithfulness evaluation—with strong cross-functional stakeholder leadership and prior large-scale fraud data pipeline experience at Socure.”

Saisureshreddy Challa

Screened

Mid-level Data Scientist specializing in AI/ML, LLMs, and domain analytics

California, USA6y exp

“BlackRock AI/ML engineer who built and owned a production LLM document intelligence system for regulatory and investment analysis end-to-end. They combined RAG, multi-agent validation, strong evaluation/monitoring, and reusable Python services to process 50K+ documents, cut review time 40-50%, and improve decision accuracy by about 25%.”

“ML/AI engineer with hands-on ownership of production GenAI and computer vision systems, spanning experimentation, deployment, monitoring, and iterative optimization. Stands out for shipping an enterprise RAG platform that cut manual review by 50% and a defect detection pipeline that reduced report generation from 15 minutes to under 1 second while maintaining high uptime and strong operational discipline.”

“Engineering leader with hands-on AI/ML systems experience spanning production inference infrastructure and consumer-facing LLM products. At Jio, they led a 17-person AI features team and delivered measurable execution gains, including 40% faster deployments and 35% lower prediction latency, while also building an end-to-end RAG-based meal recommendation product using OpenAI and Gemini.”

“UPenn robotics researcher who architected a real-time autonomous driving decision-making engine, integrating LSTM trajectory prediction with MPC in CARLA and adding conformal prediction to deliver 95% statistical safety guarantees under strict latency constraints. Also built and debugged an autonomous quadrotor stack with ESKF-based 6-DoF tracking and optimized A*/Dijkstra planning to eliminate latency-induced instability, with experience bridging heterogeneous simulation/control systems.”

“UCLA RoMeLa research assistant (since Oct 2025) building an embedded control and sensor-data platform for multi-robot coordination in a simulated warehouse. Deep hands-on experience with ROS on NVIDIA Jetson under RTOS constraints, secure MQTT/TLS telemetry, and SLAM performance optimization (including ORB-SLAM3) validated in Gazebo and deployed via Docker/Kubernetes and CI/CD.”

“Autonomous robotics student who built an end-to-end ROS2 semantic goal navigation system as a solo course project, integrating CLIP-based vision-language understanding with SLAM Toolbox and Nav2 to execute natural-language commands in Gazebo/RViz. Also implemented and tuned an RRT planner from scratch in Python and uses Docker plus GitHub workflows for reproducible, tested robotics codebases.”

“Built and deployed an enterprise GenAI knowledge assistant over thousands of internal PDFs/reports using a RAG stack (GPT-4 + Hugging Face embeddings + vector DB) to reduce manual search and SME escalations. Uses LangGraph/LangChain to orchestrate modular agent workflows with relevance filtering and fallback handling, and applies rigorous evaluation (golden datasets, edge cases, A/B tests) with production monitoring metrics.”