Vetted Microsoft Azure Professionals

“Backend/AI engineer who has shipped production LLM systems end-to-end, including an AI request-routing service (FastAPI + BART MNLI + OpenAI/Gemini) that improved accuracy ~25% after launch via eval-driven prompt/category iteration. Also built an enterprise document intelligence/RAG platform on Azure (Blob/SharePoint/Teams ingestion, OCR/NLP chunking, embeddings in Azure Cognitive Search) with PII guardrails (Presidio), confidence gating, and scalable event-driven pipelines handling millions of documents.”

“Backend-focused TypeScript/Node.js engineer who owned a production microservice for transactional workflows in a React + microservices platform, integrating REST and Kafka event processing. Emphasizes operability and correctness (idempotency keys, exponential backoff retries, DLQs, centralized logging/metrics/alerts) plus strong API DX via versioning and Swagger/OpenAPI with improved error contracts based on developer feedback.”

Raghavendra Dubey

Screened

Mid-level Software Engineer specializing in Java microservices and cloud-native systems

CA, USA5y exp

“Enterprise workflow/product engineer (DXC) who owned a customer-facing workflow application for 500+ users and improved performance ~30% through API/SQL optimization, caching, and CI/CD-backed iteration. Experienced designing React/TypeScript + Java/Spring Boot systems and operating microservices with RabbitMQ/Kafka-style messaging, emphasizing reliability via DLQs, backpressure, and strong observability. Also built an internal automation dashboard adopted by support/ops teams to cut manual work and reduce SLA misses.”

Likhith Kumar Yaramala

Screened

Junior Backend/Cloud Software Engineer specializing in microservices and DevOps

India2y exp

“Cloud/DevOps-focused engineer with strong Linux production operations experience, deploying microservices to AWS on Docker/Kubernetes. Has built and operated secure CI/CD (GitHub Actions/Jenkins) and Terraform IaC workflows with approvals, remote state, and drift detection, and has hands-on incident recovery experience in containerized environments; limited direct IBM Power/AIX/PowerHA exposure.”

“Full-stack product engineer who builds and owns production features across Next.js/React/TypeScript and Java Spring Boot, with strong Postgres data modeling and performance tuning. Has delivered measurable improvements (60%+ faster renders, 2s→100ms queries, 50% lower workflow latency) and built reliable Kafka-based workflows with robust observability (Prometheus/Grafana/Alertmanager) and high test coverage.”

Joseph Hanna

Screened

Executive IT Leader specializing in digital transformation, cloud infrastructure, and cybersecurity

Irvine, CA20y exp

“IT/technology leader (mentions VP of IT) with experience modernizing legacy environments, implementing ERP and dashboarding, and migrating on-prem infrastructure to Microsoft Azure to support remote operations. Has worked in a context described as an acquired company and focused on scaling via automation, analytics, and cross-functional alignment with marketing around lead-generation metrics.”

Muhammad Waqas Ashraf

Screened

Senior Full-Stack Engineer specializing in AI, cloud infrastructure, and DevOps

Lahore, Pakistan7y exp

“Frontend engineer focused on building and scaling data-heavy, real-time dashboards with React/Next.js/TypeScript. Emphasizes performance and reliability at scale through modular architecture, centralized state (Zustand/Redux), strict API contracts, automated testing, and production monitoring (Grafana/CloudWatch), and has experience shipping quickly with feature-flagged rollouts and rapid iteration from user feedback.”

Vedant Singh

Screened

Junior Cloud Platform Software Engineer specializing in AWS, Kubernetes, and CI/CD

Concord, CA2y exp

“Cloud/platform engineer with hands-on delivery across Azure and AWS, including standing up a CIS-compliant Azure environment and integrating Azure OpenAI Foundry to automate finance invoicing. Has scaled platform capabilities across large org footprints (dynamic CI/CD pipelines for ~94 teams across 200+ repos) and replaced a $1M/year vulnerability patching vendor by building an internal AWS-based patching and monitoring solution for ~1000 servers.”

prashanth Jamalapurapu

Screened

Mid-level AI/ML Engineer specializing in data engineering, LLM/RAG pipelines, and recommender systems

5y exp

“Research assistant at St. Louis University who built and deployed a production document-intelligence RAG system (Python/TensorFlow, vector DB, FastAPI) on AWS, focusing on grounding to reduce hallucinations and latency optimization via caching/async/batching. Also developed a personalized recommendation system for the Frenzy social platform and partnered closely with product/UX to define metrics and iterate on hybrid recommenders and cold-start handling.”

Anupam Sistla

Screened

Intern Software Engineer specializing in full-stack development, cloud, and automation

2y exp

“Robotics software engineer who built an autonomous debris-clearing rover software stack end-to-end using ROS 2, Python/OpenCV, and YOLOv3, with strong emphasis on real-time reliability (latency instrumentation, stale-data handling, watchdog fail-safes). Also implemented a Docker CI/CD deployment system for remote Raspberry Pi timelapse devices, distributing updates via AWS S3 to handle intermittent connectivity.”

Abhishek Ingle

Screened

Junior Full-Stack & AI Software Engineer specializing in React/Next.js and LLM systems

Bloomington, IN2y exp

“Backend engineer with hands-on experience building low-latency, high-concurrency real-time chat on AWS (Node.js/Socket.IO/MongoDB) and improving reliability under unstable networks, contributing to ~40% user adoption growth. Also built FastAPI-based AI assistant context retrieval (RAG) APIs with embeddings/vector search, and has strong production experience in rate-limit handling, async refactors with safe rollout, and Supabase Auth/RLS optimization.”

Ashritha G

Screened

Mid-Level Software Development Engineer specializing in distributed systems and cloud microservices

USA3y exp

“Software engineer with enterprise, customer-facing delivery experience across Outlier AI and Wipro—builds and productionizes workflow and integration solutions with a strong focus on real-world performance and reliability. Delivered a Firestore/Redis-backed real-time pipeline that cut page load times by 20% and held consistent performance across 10,000+ sessions, and has hands-on production incident experience stabilizing high-traffic microservices via caching, indexing, and safe canary deployments.”

Vaishnavi M

Screened

Mid-level AI/ML Engineer specializing in MLOps and Generative AI

5y exp

“At Liberty Mutual, built a production underwriting decision assistant combining LLM reasoning with quantitative models and strong auditability. Implemented a claims-based response verification pipeline that cut hallucinations from 18% to 3% and materially improved user trust/validation scores. Experienced orchestrating ML/LLM workflows end-to-end with Airflow, Kubeflow Pipelines, and Jenkins, including SLA-focused pipeline hardening.”

Thanmayi Ravuri

Screened

Entry Software Engineer specializing in cloud backend and microservices

Tempe, AZ1y exp

“Built production-oriented LLM agent systems for incident investigation and CRM workflows using LangGraph, FastAPI, AWS, and retrieval grounding. Stands out for treating agents like real software systems—adding schema enforcement, retries, fallbacks, monitoring, and eval loops—and tying that work to measurable gains in accuracy, latency, and analysis speed.”

Kunal Sanghvi

Screened

Mid-level Data Engineer specializing in AI, NLP, and LLM systems

USA3y exp

“Built and deployed a production AI customer support chatbot at Unique Design Inc. using FastAPI, AWS, Docker, and retrieval-based grounding on internal documents. Stands out for hands-on ownership across discovery, deployment, incident debugging, and post-launch iteration, with a strong focus on making LLM systems reliable and safe in real business workflows.”

“ML engineer with hands-on experience building banking AI systems end-to-end, including a customer-targeting model that improved campaign response rates by about 10%. Also shipped a RAG-based banking FAQ/support feature with safety guardrails and production optimizations around retrieval quality, latency, and cost, plus reusable Python services that reduced duplicate work for other engineers.”

“Built a Personal Finance Copilot, a full-stack AI assistant for transaction search, spending analysis, subscription tracking, and grounded financial Q&A, with multi-step tool-calling orchestration and hybrid retrieval/memory architecture. Stands out for using AI coding agents aggressively to accelerate planning and implementation while maintaining strong ownership of system design, testing, security, and reliability.”

Romain Delamare

Screened

Senior Full-Stack Software Engineer specializing in microservices and web applications

Montpellier, France10y exp

“Developer who treats AI as a junior collaborator, using it to accelerate mobile app feature development and UI/UX iteration while retaining architectural and implementation ownership. Has hands-on experience with specialized agents, multi-agent collaboration, and supervisor-agent patterns, suggesting practical fluency in AI-native development workflows.”

Garv Goyal

Screened

Intern software engineer specializing in AI systems and full-stack development

Vienna, VA1y exp

“Full-stack/product-minded engineer with a strong infrastructure foundation who has built both cloud automation systems and an AI voice interview coach. Stands out for combining hands-on coding with pragmatic product thinking: they improved user trust through scorecard redesign, built resilient speech handling around flaky browser APIs, and delivered measurable backend performance gains during startup internships.”

Snehitha Kola

Screened

Mid-level AI Software Engineer specializing in LLM applications and backend systems

Libertyville, IL3y exp

“Full-stack engineer with hands-on experience shipping production AI in a clinical data setting, including an end-to-end workflow that converts unstructured clinical notes into structured analytics-ready data. Stands out for combining React and backend engineering with practical LLM reliability techniques, delivering measurable gains in extraction accuracy (+30%) and analytics responsiveness (+40%).”

“Analytics-oriented candidate with hands-on GTM and sales operations experience in financial services plus applied project leadership at Northeastern. Built reporting systems in Power BI/Tableau, used Salesforce for client segmentation and campaign tracking, and created reusable launch-management tools adopted by multiple teams.”

“Full-stack engineer with healthcare/AI platform experience (Humana), owning an end-to-end high-risk patient prediction feature from React dashboards through FastAPI/TensorFlow real-time inference to AWS EKS operations. Emphasizes production reliability and contract-driven APIs (OpenAPI + generated TS types), plus strong data integration patterns (Kafka, idempotency, DLQs, backfills) in regulated, high-traffic environments.”

“Built a production LLM/RAG document extraction and game/quiz content workflow using LLaMA 2, LangChain/LangGraph, and FAISS, achieving ~94% accuracy and reducing turnaround from hours to minutes. Demonstrates strong applied MLOps/orchestration (CI/CD, MLflow, Databricks/PySpark), robust handling of noisy/variable document layouts (layout chunking + OCR fallbacks), and practical reliability practices (human-in-the-loop routing, drift monitoring, A/B testing).”

“Backend engineer with research and industry experience building data-intensive systems for healthcare and IoT. Built Python/Flask/FastAPI services with real-time ingestion and ETL into relational databases, emphasizing data quality, performance tuning, and secure access controls (JWT, RBAC, row-level filtering). Notably caught hardware-driven sensor anomalies others missed and implemented quarantine/alerting to prevent bad data from corrupting analytics.”