Vetted Microsoft Azure Professionals

“Early-career software engineer who owns revenue-critical invoice processing and internal ops tooling end-to-end. Has built TypeScript/React systems backed by MongoDB and Temporal, and designed scalable SQS-based onboarding workflows with FIFO/DLQ monitoring. Notably redesigned an Authzed SpiceDB authorization model, shrinking a 500+ line schema to ~20 lines while meeting sub-100ms p95 latency.”

“Customer-facing technical professional with Amazon incident-management experience who helps drive adoption of complex ML/LLM solutions by delivering hands-on demos and rapid model fine-tuning. Applies a disciplined debugging approach (repro + logs/metrics + severity triage) and maintains runbooks to resolve SEV2 issues in ~1 hour, while also partnering with sales/customer teams to ship patches and new features based on feedback.”

“Engineering leader (Director of Development) with hands-on cloud and product experience who builds business-aligned technology roadmaps and scales teams. Delivered an enterprise cloud-migration enabler at UHG by implementing AD authentication and Terraform-based IaC for custom VM images while meeting 90-day InfoSec patch/rotation requirements, and drove a 20% lift in user consumption/retention by designing an interactive branded media portal experience for Sunkist.”

Frank Goodman

Screened

Executive Engineering & Product Leader specializing in Cloud/SaaS observability and security

San Jose, CA31y exp

“Product/technology leader with deep security and cloud infrastructure expertise who drove a major shift from hardware-based networking/security appliances to cloud-native capabilities, growing cloud revenue from $0 to $400M in 4.5 years. Led an innovative eBPF-based approach (“precryption”) to enable lightweight cloud TLS interception/decryption, and has hands-on coding interest (recent Rust work on a personal cybersecurity identity/trust platform).”

“Candidate is a hands-on engineer using AI as a controlled coding partner rather than an autonomous decision-maker. They have practical experience designing and leading structured multi-agent coding pipelines with specialized roles for code generation, review, and test coverage, and show strong judgment around reliability through schemas, guardrails, reviewer gates, and manual validation.”

“AI/ML engineer with experience spanning Accenture healthcare NLP systems, academic research, and Apple on-device LLM integration. Stands out for owning regulated production pipelines end-to-end—from HIPAA-compliant clinical NLP and EHR integrations to incident prevention, experiment tracking, and optimized on-device inference with LLaMA 3.”

“Meta engineer who emphasizes AI-native development workflows, using Claude Code heavily to ship UI and performance fixes quickly. Notable examples include a location-aware ad relevance feature that increased CTR and revenue, and a vehicle insights chatbot whose UX improved through metric-driven prompt tuning.”

“Senior data/AI and platform leader with Walmart- and T-Mobile-scale architecture experience, including building real-time inventory + forecasting platforms (Kafka/Cassandra/Hadoop) and Azure IoT systems. Known for translating board-level business goals into roadmaps that deliver measurable impact (e.g., $50M savings and $250M profit in a year; +2% conversion via Customer 360) and for hands-on problem solving in ML/forecasting (feature reduction and LASSO).”

“Built and deployed a healthcare NLP application that used an LLM-style physician interface feeding a random forest model to predict treatment plans for hard-to-triage patient subgroups, backed by a Databricks medallion pipeline and heavy feature engineering to address missing/low-integrity data across ~50K patients. Also delivered an earlier Microsoft AI Builder automation that improved transportation bill payment workflows by training non-technical payroll/procurement teams to use automated outstanding-payables reporting.”

“Robotics engineer who worked on a lunar rover program, building a simulation environment that mirrored real hardware interfaces and incorporated moon-terrain slip/friction modeling validated against a physical “moon yard.” Also integrated an ML-based munition X-ray inspection system via REST APIs, deploying and scaling inference on Azure with Kubernetes plus Prometheus monitoring, load balancing, and self-healing reliability mechanisms.”

Dhruv Arora

Screened

Senior Generative AI Implementation Consultant specializing in RAG and agentic AI on cloud

Bay Area, CA3y exp

“LLM/RAG practitioner who built an AWS-based enterprise document search and summarization platform with RBAC and scaled it to 10K+ users, solving relevance issues via contextual chunking and hybrid retrieval. Also designed agentic workflows for a telecom forecast-validation use case using sub-agents, tool APIs, and strict context management, and has proven pre-sales influence (supported a $300K manufacturing deal with a roadmap-driven pitch).”

“Entrepreneurially driven technical leader seeking to partner with a founder/business plan owner to provide technical expertise. Helped drive Wiser's expansion into Europe by evaluating acquisition targets' technical estates and making the recommendation that was chosen. Applied lean, high-leverage product thinking at Nabis on a two-sided marketplace, delivering buyer value with a simple algorithm and later adding paid boosting for brands.”

“Graphics software engineer with academic robotics/HRI experience at Oregon State University under Dr. Heather Knight, leading a ROS+Python physical robot and Unity/C# VR system to study how motion/texture/collisions are perceived in VR (2 papers + thesis). Also built ROS-based Wizard-of-Oz TurtleBot study systems and multi-robot coordination experiments, plus industry experience with Docker/Kubeflow ML tooling and Azure DevOps CI/CD automation.”

“ML/LLM engineer with Stripe and Adobe experience who productionized a transformer-based Payments Foundation Model for real-time fraud detection at global scale (billions of transactions). Built petabyte-scale ETL/feature pipelines (Spark/EMR, Airflow, dbt, Kafka/Flink) and achieved <100ms multi-region inference (EKS, TorchServe, edge/Lambda, GPU/CPU routing) with strong PCI-DSS/GDPR compliance and explainability (SHAP/LIME), reporting a 64% fraud accuracy improvement.”

YAKKALI PAVAN

Screened

Mid-level Machine Learning & Generative AI Engineer specializing in NLP, CV, and RAG systems

USA6y exp

“Built and deployed a production LLM-powered RAG document intelligence system used by non-technical enterprise stakeholders, cutting document search time by 40%+ while improving answer consistency. Demonstrates strong MLOps/data workflow orchestration (Airflow, AWS Step Functions, managed schedulers across GCP/Azure) and a metrics-driven approach to reliability, evaluation, and cost/latency optimization with guardrails and observability.”

BHABANI Dash

Screened

Principal Enterprise Architect specializing in AI, cloud strategy, and digital transformation

Lake Villa, IL20y exp

“Aspiring AI product builder interested in LLMs and deep learning, exploring forming a team (including fresh graduates) and leveraging crowdsourcing to develop ideas. Has not raised capital and has no VC/accelerator experience yet, but is thinking ahead about funding needs and partnering with an operational co-founder while potentially joining an existing team.”

“Fintech engineer with high-ownership startup experience at Column, where they led a Go rewrite of a core banking API that cut transaction errors by 98% and increased throughput by 40%. Brings a rare mix of backend systems depth in compliance-sensitive money movement infrastructure and frontend platform experience building React design systems, with earlier experience at Meta on recommendation APIs and News Feed.”

“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”

“ML/NLP engineer with experience building production-grade legal-tech and data platforms, including a GPT-4/LangChain contract review system using ElasticSearch embeddings (RAG) deployed on AWS EKS. Strong in entity resolution and scalable batch/streaming pipelines (Kafka/Spark), with measurable impact (70%+ reduction in contract review time) and a focus on monitoring and CI/CD for reliable delivery.”

“ML/robotics engineer with Apple experience who built a computer-vision-driven industrial defect detection system integrating a robotic arm with ROS-based real-time inference on an edge GPU. Drove major performance gains (cut inference time ~60% via quantization + TensorRT) and improved robustness to lighting/material variation, with strong emphasis on production reliability (health checks, watchdogs, observability, CI/CD) and interest in shaping early-stage startup engineering culture.”

Sandeep Rohilla

Screened

Principal Backend/Platform Engineer specializing in GenAI agent orchestration and LLM pipelines

San Francisco, CA19y exp

“LLM-focused engineer/sales-engineering profile with hands-on experience productionizing complex systems: scalable distributed architecture, multi-tenant monitoring, canary/shadow rollouts, and robust fallback strategies. Demonstrated real-time troubleshooting depth (p99 latency spikes traced to DB connection limits causing retry storms) and strong developer-facing communication via RAG workshops and live, customer-specific demos that helped close deals quickly.”

“Built and shipped production AI systems spanning customer support automation at Uber, privacy-preserving federated health modeling on iOS, and an open-source semantic search layer for Postgres. Stands out for combining strong LLM/product instincts with rigorous eval design, measurable production impact, and zero-to-one execution across backend, mobile, and developer infrastructure.”

“Built and owned end-to-end deployment and AI support workflows spanning CI/CD, Kubernetes, Terraform, and LLM/RAG systems. Stands out for combining DevOps delivery with production AI operations, including secure tool-calling, incident debugging, retrieval quality controls, and validation-first document ingestion for messy real-world inputs.”