Vetted Data Preprocessing Professionals

“Full-stack engineer with payments/settlement domain experience who modernized a payment tracking workflow from REST to GraphQL and delivered a production payment status dashboard using Next.js App Router + TypeScript. Strong in performance and reliability work (Postgres indexing/Explain Analyze, Redis caching, Datadog observability) and in durable event-driven processing with Kafka (DLQs, idempotency, reconciliation, event replay).”

“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”

“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”

“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”

“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.”

“Software engineer with hands-on experience building data-intensive and 3D-processing web applications (React/Next.js/TypeScript + Node.js). Has worked in microservices using RabbitMQ for event-driven workflows and built an internal ops/engineering dashboard to monitor pipeline jobs, surface logs, and manage retries—improving visibility and reducing on-call/debug time.”

“Robotics engineer who built and scaled a distributed perception stack on a Unitree Go1 quadruped, coordinating 5 Jetson Nanos and a Raspberry Pi to capture, aggregate, and stream multi-camera video in real time via UDP/GStreamer and custom ROS nodes. Also implemented a YOLOv9-based detection pipeline enhanced with Grad-CAM-driven selective image enhancement (e.g., MIRNet/UFormer) to improve real-time detections and robot reactions to visual stimuli.”

Pranathi Kamisetty

Screened ReferencesStrong rec.

Intern AI Engineer specializing in LLMs, NLP, and conversational search

Chicago, Illinois1y exp

“Student building a production trip-planning LLM agent (LangChain + Streamlit) that routes user queries across multiple tools (maps/places/Wikipedia). Implemented zero-shot multi-label intent detection with priority rules to handle multi-intent requests, and collaborates with a startup product manager to shape tone, features, and user experience.”

“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).”

“Healthcare analytics professional with hands-on experience turning messy claims, eligibility, and utilization data into validated BI-ready models using SQL and Python. They combine strong data engineering and KPI design skills with stakeholder-facing delivery, including Power BI prototyping, retention metric operationalization, and analyses that supported care management interventions and cost-control decisions.”

“Hands-on LLM/RAG engineer with production experience improving retrieval quality and stability by addressing messy data, vector DB inaccuracy, and top-K issues—ultimately redesigning to hybrid search with tuned keyword/semantic weighting and MCP-based data supplementation. Also brings strong AKS/Kubernetes deployment experience, optimizing CI/CD speed via lightweight local Docker validation and decomposing pods to avoid full rebuilds, plus a metrics-driven approach to agent/workflow testing and traceability.”

“Robotics software engineer who led an autonomous nursery management robot project at Auburn University, spanning RGB-D/IMU sensor fusion, SLAM navigation, and real-time ML for plant detection/quality assessment. Strong ROS1/ROS2 background (C++/Python) with deployment on NVIDIA Jetson, including profiling-driven optimization of YOLO segmentation for real-time behavior and multi-robot (UGV/UAV) communication using ROS2.”