Vetted Random Forest Professionals

“Built a production LLM-powered clinical note summarization and retrieval system that structures patient/provider/payer discussions into standardized outputs (symptoms, treatments, clinical codes, and prior-auth decisions) and stores notes as embeddings for hybrid search and proactive prior-authorization prediction. Experienced with LangChain/LangGraph orchestration, RAG, and grounding against medical code databases, and has communicated model feasibility/limitations to business stakeholders (Virtusa/Comcast).”

“Recent master’s graduate in robotics with applied experience across reinforcement learning and ROS 2 autonomy stacks. Built an RL-based drone vertiport traffic controller (PPO) focused on reward design and simulation integration, and has hands-on navigation work in ROS 2 including LiDAR preprocessing, SLAM/path planning, and stabilizing TurtleBot3 wall-following. Also brings deployment experience containerizing robotics nodes and scaling them with Kubernetes on AWS.”

“ML/NLP engineer focused on large-scale data linking for e-commerce-style catalogs and customer records, combining transformer embeddings (BERT/Sentence-BERT), NER, and FAISS-based vector search. Has delivered measurable lifts (e.g., +30% matching accuracy, Precision@10 62%→84%) and built production-grade, scalable pipelines in Airflow/PySpark with strong data quality and schema-drift handling.”

“AI/LLM practitioner who has deployed production chatbots across e-commerce, HRMS, and real estate, focusing on retrieval-first workflows for factual tasks like product and property search. Optimized intent understanding and significantly improved latency by using lightweight embeddings and tuning the inference pipeline on Groq (Llama 3.3), while applying modular orchestration and measurable production evaluation.”

“Full-stack product engineer who has shipped customer-facing features end-to-end, including a product detail page backed by Java/Spring Boot microservices and a React/TypeScript UI. Demonstrated measurable impact through performance and maintainability improvements (30% faster APIs, 25% less duplicated UI code, 40% reduced API complexity via GraphQL) and has operated/scaled apps on AWS with CI/CD, monitoring, and incident-driven scaling fixes.”

“AI/ML engineer who shipped a production blood-test report understanding and personalized supplement recommendation product, using a LangGraph multi-agent pipeline on AWS serverless with OCR via Bedrock and RAG over vetted clinical research. Also built end-to-end recommender system pipelines at ASANTe using Airflow (ingestion, embeddings/features, training, registry, batch scoring/monitoring) with KPI reporting to Tableau, with a strong focus on safety, evaluation, and measurable reliability.”

“Data science/AI intern at University at Buffalo Business Services who built and deployed production systems spanning classic ML and LLM assistants. Delivered real-time competitor intelligence for a Cornell-partnered, $1B beverage launch by scraping/cleaning 5,000+ SKUs and deploying models via API, then built a domain-aware LLM assistant to modernize Excel-based workflows with strong grounding, privacy controls, and sub-5s latency.”

“Software engineering intern experience at Zoho Corp and Zeus Desk building and deploying customer-facing systems. Delivered a real-time booking platform backend that stayed stable for 1,000+ users by optimizing MySQL queries/indexing and shipping hotfixes during production latency incidents. Also integrated financial operations APIs across 50+ small-bank partners by creating a normalization/validation layer to handle inconsistent partner data and prevent integration breakages.”

“Robotics software engineer with deep ROS2 experience who owned the perception stack for an automated C. elegans manipulation system—building YOLO-based worm segmentation plus OCR label reading and integrating it into a MoveIt2 pipeline with real-time latency constraints. Also deploying ROS2 on an AgileX Tracer with ZED depth camera for vision-based person following and working on SLAM/sensor fusion, with additional production-style ML deployment experience (Dockerized FastAPI + PyTorch on AWS EC2 with CI/CD).”

“AI/ML manager who has deployed production NLP in healthcare—mining unstructured clinical notes and combining them with structured patient data to predict readmissions, with strong emphasis on data alignment and terminology normalization. Also experienced operationalizing ML with Airflow/MLflow and AWS Step Functions/SageMaker, plus stakeholder-facing Power BI dashboards (e.g., marketing customer segmentation).”

“Backend/platform engineer with payments and insurance domain experience (Cognizant), owning high-volume production systems end-to-end. Shipped a Spring Boot payment tokenization service with strong observability and phased migration that cut transaction latency ~30% and improved payment efficiency ~25%. Also productionized an ML-driven financial health/risk analytics pipeline with near real-time dashboards across 70+ schools, emphasizing interpretability, data quality, and drift monitoring.”

“AI Engineer at Humanitarian AI who has built and productionized both a LangGraph-based multi-agent workflow system and a RAG pipeline (OpenAI embeddings + vector DB) with rigorous evaluation/guardrails. Reports strong measurable impact (60% faster workflow delivery, 40% fewer incidents, 70% reduced research time) and has prior enterprise modernization experience at Infosys migrating ETL to microservices with zero production incidents.”

“Backend engineer who has designed and evolved high-traffic event/activity management systems using Node/Express and PostgreSQL, prioritizing scalability and reliability with a layered architecture. Has led zero-downtime refactors/migrations using parallel runs, dual writes, and rigorous validation/monitoring, and brings a security-focused API approach (JWT, RBAC/ABAC, rate limiting, DB-enforced tenant/RLS filters).”

“Master’s thesis researcher building an intelligent fault diagnosis and predictive maintenance stack for autonomous quadcopters—covering simulation-based fault injection, signal processing (Id/Iq), ML fault classification, and real-time edge deployment on Raspberry Pi with Hailo-8 acceleration. Previously delivered production C++ middleware/microservices at Accolite and has hands-on experience with constrained networking via a LoRaWAN IoT communication stack.”

“Built and deployed an agentic RAG platform at Centene Health to support healthcare claims and complaints workflows (Q&A for claims agents, executive complaint summarization, and compliance triage/classification). Experienced in LangChain/LangGraph orchestration, production deployment on AWS with FastAPI/Docker/Kubernetes, and implementing HIPAA-compliant guardrails to reduce hallucinations and ensure explainable outputs.”

“Accenture engineer who built and shipped a production RAG-based automation/chatbot for SAP incident triage and troubleshooting, embedding thousands of runbooks/logs/tickets into a semantic search pipeline and integrating it into Teams/Slack. Reported major productivity gains (30–60% time reduction), >90% validated answer accuracy, and sub-2-second responses, with strong orchestration (Airflow/Prefect/LangGraph) and reliability practices (guardrails, testing, monitoring).”

“ML/NLP engineer with banking domain experience who built a GenAI-powered fraud detection and risk intelligence system at Origin Bank, combining RAG (LangChain + FAISS), fine-tuned BERT NER, and GPT-4/Sentence-BERT embeddings. Delivered measurable impact (25% higher fraud detection accuracy, 40% less manual review) and emphasizes production-grade pipelines on AWS SageMaker/Airflow with strong data validation and scalable PySpark processing.”

“Backend engineer who built an Emergency Alert System with Virginia Tech for the City of Alexandria, focusing on real-time ingestion, secure dashboards, and AI-assisted prioritization. Emphasizes high-stakes reliability with guardrails (hybrid rules+LLM, confidence-based fallbacks), scalable async processing, and defense-in-depth security (JWT/RBAC plus database row-level security).”

“Backend/AI engineer who has shipped production LLM systems end-to-end, including an AI request-routing service (FastAPI + BART MNLI + OpenAI/Gemini) that improved accuracy ~25% after launch via eval-driven prompt/category iteration. Also built an enterprise document intelligence/RAG platform on Azure (Blob/SharePoint/Teams ingestion, OCR/NLP chunking, embeddings in Azure Cognitive Search) with PII guardrails (Presidio), confidence gating, and scalable event-driven pipelines handling millions of documents.”

“Robotics software builder who delivered an end-to-end gesture-controlled drone system using an ESP32+IMU stream and real-time ML inference mapped to Tello SDK commands. Drove reliability improvements by instrumenting the pipeline with timestamps/logging and matching training vs runtime preprocessing, reaching ~94% gesture classification accuracy; experienced with Docker/Compose for reproducible multi-service deployments.”