Vetted AI & Machine Learning Professionals in Texas

Ritika Ghosh

Screened ReferencesStrong rec.

Junior Robotics & AI Engineer specializing in ROS2 autonomy and real-time computer vision

Dallas, US3y exp

“Robotics software engineer from Stanley Black & Decker’s autonomous team who built and deployed a ROS2-based model predictive control system for a commercial autonomous lawn mower, integrating real-time localization, Nav2 planning, and custom control under real-time constraints. Has hands-on field debugging experience (Foxglove, TF timing, covariance/noise tuning) to resolve issues that only appeared outside simulation, plus containerized deployment and CI/CD experience.”

Kevin Allen

Screened

Senior AI/ML Engineer specializing in conversational and generative AI

Austin, TX12y exp

“Built and productionized an LLM-based support assistant end-to-end, including RAG, APIs, monitoring, guardrails, and agent feedback loops. Stands out for translating GenAI prototypes into reliable production systems with structured evaluation, safety controls, and reusable Python infrastructure that improved both support quality and engineering velocity.”

“ML/AI engineer with hands-on ownership of production systems spanning classical ML fraud detection and GenAI agent workflows. At Fidelity, they built an end-to-end fraud platform that improved review queue Precision@K by 15-20% while reducing false positives 10-15%, and they also shipped RAG-based agent systems that cut manual workflow effort by 30-40%.”

Pranav Puranik

Screened

Senior AI Engineer specializing in LLMs, RAG, and multimodal NLP

Austin, TX5y exp

“Built a production LLM/RAG assistant for insurance/health claims agents that ingests 100–200 page patient PDFs via OCR (migrated from local Tesseract to Azure Document Intelligence) and delivers grounded claim detail retrieval plus summaries with PII/PHI guardrails. Experienced orchestrating large workflows with Celery worker pipelines and AWS Step Functions (S3-triggered, Fargate-based batch inference/accuracy aggregation), and collaborates closely with non-technical SMEs (claims agents/nurses) through shadowing, iterative demos, and SME-defined evaluation.”

“Built and deployed a production generative AI chatbot at NVIDIA using LangChain + GPT-3 integrated with internal data sources, cutting response time nearly in half and improving CSAT by ~12 points. Also delivered LLM-driven QA tools by fine-tuning Hugging Face transformer models and deploying via an AWS-based pipeline (Lambda/Glue/S3) with orchestration (Airflow/Step Functions), CI/CD, Kubernetes, and monitoring (MLflow/Splunk/Power BI).”

“Built a production LLM/RAG-based “model excellence scoring” system at Uber to automatically evaluate hundreds of ML models, standardizing quality assessment and cutting evaluation time from days to minutes on GCP. Also delivered an NLP document classification solution for insurance claims at Globe Life, partnering closely with compliance/operations and improving routing accuracy from ~85% manual to 93% with the model.”

“ML/LLM engineer building production, real-time fraud detection for financial transactions using a two-tier architecture (fast ML + GPT) to deliver both low-latency decisions and analyst-friendly risk explanations. Experienced orchestrating end-to-end retraining, drift monitoring, and automated model promotion with Databricks Jobs/Workflows and MLflow, and partnering closely with fraud analysts to tune alerts, thresholds, and dashboards.”

“LLM/agentic systems builder at Verizon who deployed a LangGraph-orchestrated multi-agent ticket-automation platform with RAG (FAISS) to replace brittle rule-based bots. Improved routing correctness by ~30–40%, hit ~300ms latency targets via model routing, and reduced ops workload by ~60% through tight iteration with non-technical stakeholders and strong testing/observability practices.”

Yash Patel

Screened

Mid AI/ML Engineer specializing in MLOps and cloud platforms

Texas, USA3y exp

“ML/AI engineer with strong end-to-end production ownership across classical ML and GenAI systems. Built and deployed predictive analytics and RAG-based internal tools on AWS/Kubernetes with measurable impact on accuracy, latency, deployment speed, safety, and user productivity.”

“GenAI/ML engineering leader (player-coach) who built and deployed an image-to-text production system for topology/resource diagrams, combining YOLO-based issue detection with an LLM to generate support-ready reports at scale. Heavy AWS stack (SageMaker, Step Functions, Lambda, CloudWatch, FastAPI, Kubernetes/Docker) with KPI-driven optimization (MTTR, P50), including ~21 custom labels and reported 30–50% faster issue identification while processing thousands of images in production.”