Vetted LSTM Professionals

“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.”

“AI/ML engineer with production experience building LLM-powered document intelligence and customer support systems in healthcare/insurance, emphasizing high-accuracy RAG, long-document processing, and robust monitoring/fallback mechanisms. Also automates and scales ML lifecycle workflows using Apache Airflow and Kubeflow, and partners closely with non-technical operations stakeholders to drive adoption.”

“Developed a production wildfire smoke detection system where smoke is visually subtle and easily confused with fog/clouds; addressed this with a hybrid CNN+LSTM+ViT model and multimodal weather features to reduce false positives. Experienced running scalable, reproducible ML pipelines on shared GPU infrastructure using Slurm and Kubernetes-style batch jobs with checkpointing, retries, and rigorous error analysis.”

“Red Hat ML/LLM engineer who designed and deployed a production LLM-powered customer support automation system using RAG, improving latency by 30% via PEFT and vector search optimization. Built security and governance into retrieval (access-level filtering, encrypted Pinecone/ChromaDB) and delivered SHAP-based explainability via a dashboard for non-technical stakeholders. Experienced orchestrating distributed ML/RAG pipelines across AWS SageMaker and OpenShift with Airflow/Prefect, plus multi-agent workflows using CrewAI and LangGraph.”

“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).”

“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.”

“Robotics/control-focused engineer who built and validated a series elastic actuator control stack end-to-end (dynamic modeling, torque/position control, simulation, and experimental real-time debugging on hardware). Deep simulation background (OpenFOAM/COMSOL/Abaqus) and practical reproducibility tooling (Docker/CI), with conceptual ROS/ROS2 knowledge and confidence ramping into ROS-based stacks.”

“Built and deployed a production LLM/RAG clinical decision support system that enables real-time semantic search over unstructured EHR notes and delivers patient risk insights. Strong in healthcare-grade MLOps and compliance (HIPAA, PHI handling, encryption, RBAC, audit logs) and scaled embedding/retrieval pipelines using Spark/Databricks and Airflow. Partnered with clinicians via Power BI dashboards and explainability, contributing to an 18% reduction in patient readmissions.”

“Built and deployed profitprops.io, a sports betting player-props prediction product using ML/AI. Implemented backend APIs with FastAPI/Express.js and Supabase, trained models on AWS GPU (P3) using Docker + RAPIDS, and set up CI/CD with GitHub Actions while working around cost constraints and data-collection hurdles (EC2 proxy rotation/rate limits).”

“Built and productionized an LLM-powered PDF document Q&A system to eliminate manual searching through long documents, focusing on scalability and answer reliability. Implemented semantic chunking (using headings/paragraphs/tables), overlap, and preprocessing/quality checks to reduce hallucinations, and orchestrated the end-to-end pipeline with Airflow using retries, alerts, and parallel tasks.”

“AI/ML engineer with experience at Accenture and Morgan Stanley, building production LLM systems (GPT-3 summarization) and finance-focused ML models (credit risk and trading anomaly detection). Combines MLOps depth (Docker/Kubernetes, AWS SageMaker/Glue/Lambda, MLflow, A/B testing, drift monitoring) with practical domain adaptation techniques like few-shot prompting and RAG/knowledge-base integration.”

“GenAI Engineer at Infosys who built and deployed a production multi-agent RAG system for a top-tier bank, scaling to ~50,000 queries/day with 99.9% uptime. Drove measurable gains (45% accuracy improvement, 30% API cost reduction) through open-source LLM fine-tuning, Pinecone indexing/retrieval optimization, and AWS-based MLOps/monitoring, and has experience enabling adoption via developer workshops and customer-facing collaboration.”

“Applied ML to physical EV fleet systems at ST Labs, building a real-time CNN-LSTM fault prediction pipeline from streaming vehicle telemetry and addressing live data alignment issues via resampling/interpolation and buffered inference. Also developed a V2G/G2V energy transfer algorithm to automate charging/discharging for profit optimization, and made high-impact low-latency pipeline decisions at Astera Holdings using profiling, replay testing, and live A/B validation.”

“Healthcare/clinical ML practitioner who built and productionized ClinicalBERT-based pipelines to extract and standardize oncology EHR data, improving downstream model F1 from 0.81 to 0.92 while controlling training cost via LoRA/QLoRA. Experienced orchestrating real-time AWS ETL/ML workflows (Glue, Lambda, SageMaker) and partnering with clinicians using SHAP-based interpretability, contributing to an 18% reduction in readmissions and full adoption.”

“Data/ML practitioner with experience applying NLP and classical ML to large-scale customer data (2B+ records) for segmentation, prediction, and survey-text classification, delivering measurable business impact (~18% engagement efficiency). Has hands-on entity resolution across multi-source datasets and has built embedding-based semantic search using SentenceBERT + a vector database with domain fine-tuning (~20% relevance improvement), plus production workflow experience with Spark/Airflow and cloud tooling (AWS/Azure).”

“AI/software engineer with experience at Visa building a real-time transaction fraud/risk scoring microservice in the card authorization path (Python, Kafka, Kubernetes on AWS) with strict 120–150ms latency constraints and reason-code outputs for downstream decisioning. Owns ML backend end-to-end (data/feature engineering, model training, deployment) and has demonstrated production reliability work including latency spike mitigation, SLO-based observability, drift monitoring, and safe fallbacks to rule-based decisions.”

“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.”

“Built and deployed an agentic RAG assistant in production to automate enterprise knowledge search and multi-step workflows with tool calling, tackling real-world issues like hallucinations, retrieval accuracy, and latency. Demonstrates strong LLMOps and orchestration depth (MLflow, Airflow, LangGraph/LangChain/LlamaIndex) plus a metrics-driven approach to agent testing/evaluation and cross-functional delivery with business stakeholders.”

“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.”

“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.”

“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.”

“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.”