Vetted Azure Data Factory Professionals

Brian Sanders

Screened

Senior Backend Engineer specializing in GenAI, LLMs, and scalable data pipelines

Chicago, IL12y exp

“Backend/ML platform engineer from Snapsheet who owned production Python services and data pipelines for insurance claims, including an AI document classification/summarization FastAPI service on ECS/Fargate processing 1M+ documents/year. Strong in AWS infrastructure (Terraform, CI/CD, secrets/IAM, autoscaling), Glue/PySpark ETL with schema evolution controls, and legacy SAS-to-microservices modernization with safe, feature-flagged rollouts and measurable performance wins.”

“Senior, product-focused engineer who has built real-time customer-facing web applications and a microservices backend (TypeScript/React/Node) using RabbitMQ, MongoDB, and Redis. Demonstrates strong operational maturity (idempotency, tracing/observability, backpressure) and built an internal console that became the primary tool for debugging, replaying jobs, and managing system behavior.”

Parth Vadera

Screened

Senior AI/ML Data Scientist specializing in recommender systems, LLMs, and MLOps

Tracy, CA11y exp

“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”

Ravikanth Kasamsetty

Screened

Executive engineering leader specializing in AI, data platforms, and cloud SaaS

California, USA23y exp

“Senior engineering executive with a rare mix of VP-level people leadership and deep hands-on architecture across cloud-native and AI platforms. He led secure hybrid SaaS design at Promethium, built GenAI-powered analytics with LLMs/RAG, and translated customer needs into product wins including the largest deal in company history and a PLG motion that generated 100+ leads.”

“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”

“Robotics-focused engineer with ROS 2 experience who has built and debugged real-time, distributed control/orchestration systems under production-like latency and safety constraints. Led platform changes at Persona for a real-time verification orchestration system using deterministic state machines and async workers, and has hands-on experience stabilizing multi-robot navigation/SLAM behavior using rosbag, RViz, and stress testing in simulation (Gazebo).”

Shailaja Domala

Screened

Executive product and AI leader specializing in data platforms and analytics

California, USA24y exp

“Engineering leader with deep experience at Visa building and modernizing large-scale analytics platforms, including refactoring legacy systems into globally available microservices on AWS. Combines hands-on technical judgment in architecture, search platform evaluation, and service reliability with management of distributed international engineering and data teams.”

“ML/AI engineer with hands-on ownership of production computer vision and GenAI systems, spanning real-time public safety video analytics and RAG-based knowledge assistants. Stands out for translating research-oriented approaches into scalable, monitored production systems with clear business impact, including 50% latency reductions, 25% faster response times, and 40% lower document search time.”

“Built and shipped an AI-powered resume analysis and job-matching product end to end across React, TypeScript, FastAPI, OpenAI, LangChain, and FAISS. Strong in practical LLM systems design, including RAG, structured outputs, evals, monitoring, and human-in-the-loop decision support, with a reported 35% improvement in recruiter-validated matching accuracy.”