Vetted pandas Professionals

“ML/AI engineer with hands-on experience shipping production computer vision and GenAI systems, including a fabric defect detection platform that combined vision models with agentic LLM workflows to reach 89% human-inspector agreement at 200 ms latency. Also built a RAG-based code QA tool for developers and emphasizes production monitoring, evaluation, caching, and reusable Python service design.”

“AI/ML Engineer (TCS) who built and deployed a production LLM-powered audit transaction validation service to reduce manual review of unstructured transaction records and comments. Implemented a LangChain/Python pipeline for extraction/normalization and discrepancy detection, with strong production reliability practices (decision logging, dashboards, labeled eval sets) and a human-in-the-loop auditor feedback loop to improve precision/recall under strict data-sensitivity and near-real-time constraints.”

“Built a production LLM-powered fraud detection platform at Wells Fargo, combining OpenAI/Hugging Face models with RAG-based explanations to make flagged transactions interpretable for risk and compliance teams. Delivered low-latency, real-time inference at high scale on AWS (SageMaker + EKS), with strong observability and security controls, reducing manual reviews and false positives in a regulated environment.”

“Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.”

“Robotics software engineer/researcher focused on perception, SLAM, and sensor fusion, with hands-on experience taking systems from simulation to embedded/real-time deployment. Led transparent-surface (glass) detection using GDNet and achieved a major real-time speedup (~7–9 FPS to ~30 FPS) while preserving >90% recall, and has built ROS-based EKF GPS-IMU fusion plus profiled/optimized Visual SLAM for performance and memory stability. Also brings production-style deployment skills via Docker/Kubernetes orchestration of ML inference services with autoscaling and model update rollouts.”

“ML/AI practitioner in healthcare (Syneos Health) who has deployed production clinical NLP and risk models. Built a BERT-based physician-note information extraction system on Docker + AWS SageMaker (reported ~42% retrieval improvement) and automated retraining/deployment with Airflow and drift detection, while partnering closely with clinicians to drive adoption (reported ~18% readmission reduction).”

“Master’s student and Deep Learning teaching assistant who teaches LLM/VLM fine-tuning (including LoRA) and built a Hugging Face LLM fine-tuned for unit conversion, improving reliability by analyzing synthetic data and filling missing number-system conversion examples. Also implemented the Raft consensus protocol using gRPC in a distributed systems course with correctness validated by unit tests.”

“Built and deployed a GPT-4-powered medical assistant for clinical staff to reduce time spent searching guidelines and EHR information, with a strong emphasis on safety and compliance. Uses strict RAG, confidence thresholds, and fallback behaviors to prevent hallucinations, and runs production-grade workflows orchestrated with LangChain/LangGraph plus Docker/Kubernetes/MLflow and monitoring for reliability and cost.”

“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”

“Cloud-native integration engineer (Oracle/OCI) with strong production deployment and incident-response experience, including API gateway rollouts, observability (Prometheus/Grafana), and multi-layer debugging for payments systems. Built Python/FastAPI microservices and automation for customer-specific reporting and data sync, and has delivered major performance gains (45 min to <10) plus reliability improvements (MTTD reduced 40%+) through monitoring, playbooks, and resilient integration patterns (streaming/queuing, retries, secure tokens, VPC peering).”

“LLM/agentic workflow specialist with healthcare deployment experience who has taken LLM-based automation from prototype to production using operator-in-the-loop validation, RAG-style retrieval, RBAC, and monitoring for sensitive data compliance. Demonstrated real-time incident resolution (retrieval timeouts due to network/proxy misconfig) and strong GTM support—hands-on developer workshops and sales demos translating technical safeguards and real-time ETL into measurable ROI (70% ops reduction, ~$200K/year savings).”

“Built a production real-time anomaly detection platform for high-frequency trading at HSBC, using a streaming stack (Pulsar + Spark Structured Streaming + AWS Lambda) and a transformer-based model combining time-series and numerical signals. Experienced in MLOps and safe deployment (Kubernetes, canary releases, MLflow/Grafana monitoring) and in aligning model performance with risk/compliance expectations through SLA-driven tuning and stakeholder-friendly dashboards.”

“Co-founded a healthcare AI startup building and deploying software directly with end users, emphasizing rapid shipping, deep user interviews, and workflow-first adoption. Has hands-on production deployment experience on AWS (including diagnosing a silent AWS App Runner failure caused by an ARM vs amd64 Docker build mismatch) and is motivated by customer-facing, travel-heavy roles to keep engineering tightly connected to real-world usage.”

“Built a production RAG-based “unified assistant” that consolidates siloed company documents into a single chatbot while enforcing fine-grained access control via RBAC/metadata filtering with OAuth2/JWT. Experienced orchestrating LLM workflows with LangChain/LangGraph + FastAPI (async + caching) and measuring performance via retrieval accuracy and response-time SLAs. Also delivered a churn analytics solution with dashboards and automated retention campaigns using n8n.”

“ML/NLP engineer who built a RAG-based technical assistant for Caterpillar field engineers, transforming PDF keyword search into intent-based semantic retrieval across manuals, logs, sensor reports, and technician notes. Strong in productionizing data/ML systems (Airflow, PySpark) with rigorous preprocessing, entity resolution, and evaluation—delivering measurable gains in accuracy, relevance, and duplicate reduction.”

“Applied NLP/ML in healthcare and financial services, including fine-tuning BERT on unstructured EHR text and building embedding-based similarity search for clinical concepts. Also redesigned a Wells Fargo fraud detection data pipeline using modular Python + AWS Glue/Step Functions, cutting runtime ~40% with improved monitoring and reliability.”

“AI/ML engineer at Cigna Healthcare building a production, HIPAA-compliant LLM-powered clinical insights platform that summarizes unstructured medical notes using a fine-tuned transformer + RAG on AWS. Demonstrates strong end-to-end MLOps and cloud optimization (distillation, Spot/Lambda/Auto Scaling) with quantified outcomes (~28% accuracy lift, ~40% less manual review, ~25% lower ops cost) and strong clinician-facing explainability via SHAP and dashboards.”

“Currently at Huntington Bank, built a production-grade RAG system that helps business/operations teams get grounded answers from large volumes of internal enterprise documents. Owns ingestion and FastAPI backend, tuned hybrid BM25+vector retrieval and chunking for relevance, and evaluates reliability with metrics and observability (LangSmith, CloudWatch, Prometheus/Grafana) while partnering closely with non-technical stakeholders.”

“Software engineer with experience across TCS, Rakuten, and USC who has owned production integrations and data pipelines end-to-end. Notably improved a trading platform payment flow by replacing fragile polling with a webhook-driven status system with robust fallbacks, and has shipped LLM-assisted design-to-webpage automation plus evaluation-driven prompt iteration (NYT Connections).”

“Full-stack engineer with fintech trading-platform experience who shipped and operated a real-time portfolio P&L/performance feature end-to-end (React + Node/WebSockets + MongoDB) on AWS, including significant performance tuning under peak trading load. Also built a Spark-based trading analytics pipeline with idempotency and reconciliation for auditability, and has a personal React/TS + Node/Express project (Artsy) with JWT auth and schema-evolution practices.”

“Data engineer in healthcare (HCA) who owned end-to-end Azure-based pipelines at very large scale (50M+ daily claims/patient records). Strong focus on reliability: schema-drift fail-fast validation, quarantine layers, and Python/SQL data quality checks that reduced issues ~25%, plus performance tuning in Databricks/PySpark and versioned serving in Synapse for downstream consumers.”