Vetted Microsoft Azure Professionals

“Full-stack Java engineer (4+ years) who led end-to-end modernization of high-latency order management systems into cloud-native reactive microservices (Spring WebFlux) and built real-time React/Redux dashboards, reporting 99.98% uptime and 22% infra cost savings. Also headed a production RAG-based Order Support Bot at Dell Technologies with embeddings + MongoDB semantic search, automated validation and human fallback, plus CI/CD-driven LLM eval loops to reduce hallucinations.”

“ML/LLM practitioner who has deployed a production RAG-based trouble-call identifier using multiple datasets (device, network, past complaints). Experienced in end-to-end MLOps (FastAPI + Docker + Kubernetes with HPA) and in evaluating/monitoring LLM behavior to reduce hallucinations, with additional applied work in forecasting/anomaly detection and churn prediction for retention campaigns.”

“Data/MLOps engineer (Cognizant background) who owned an AWS/Airflow/Snowflake healthcare transactions pipeline processing ~8–10M records/day and cut pipeline/data-quality incidents by ~33%. Also built and deployed a production FastAPI model-inference service on Kubernetes (Docker, HPA) with strong observability (Prometheus/Grafana), versioned endpoints, and resilient backfill/idempotent external data ingestion patterns.”

“Backend/data engineer focused on AWS-based, event-driven systems—building Golang microservices and serverless pipelines with strong data validation, observability (CloudWatch/Splunk/New Relic), and reliability patterns (retries/DLQs). Has also operated distributed web scraping/data collection with schema versioning and Step Functions backfills, and ships well-documented, versioned REST/WebSocket APIs for internal and external consumers.”

“Data engineer with experience at Wells Fargo and Accenture owning end-to-end production pipelines processing hundreds of millions of transactional/risk records daily. Strong focus on data quality and reliability (reconciliation checks, schema drift detection, CloudWatch alerting) plus Spark performance tuning and idempotent backfills using Delta Lake/merge logic across AWS (S3/EMR/Databricks/Redshift) and Azure (ADF/Azure DevOps/Azure Monitor).”

“Data engineer with experience across healthcare and geospatial risk systems, owning end-to-end pipelines from ingestion through serving on AWS/Azure stacks. Built HIPAA-compliant data quality gates and CDC for millions of daily claims, and also delivered a real-time wildfire risk platform with 20-minute refresh cycles and a 60% data accuracy lift. Strong in streaming (Kafka), Spark performance tuning, and production-grade orchestration/CI/CD (Airflow, Docker, Jenkins, GitHub Actions, Terraform).”

“Data engineer/backend engineer with experience in healthcare (Cardinal Health provider enrollment) and finance (Northern Trust) building and stabilizing data pipelines and REST services. Worked with APIs and Kafka at ~200k–300k records/day, improving data quality (DLQ + validation), performance (SQL/indexing), and reliability/observability (logging, alerts, consumer lag metrics), and stood up an early-stage financial data service with Jenkins-based CI/CD.”

“Data engineer at CenterPoint Energy who built and operated multiple production-grade GCP data systems: a daily Snowflake→BigQuery replication framework (150+ tables) with Monte Carlo/Atlan-driven observability and schema-drift protection, plus a FastAPI metrics service for pipeline health. Demonstrated measurable impact (40% faster dashboard queries, 70% less manual refresh work, zero data loss) and strong operational rigor (scaling Cloud Run jobs, SAP SLT reconciliation, quarantine patterns, CI/CD via GitHub Actions + Terraform).”

Rushir Bhavsar

Screened

Intern AI/ML Engineer specializing in LLMs, MLOps, and distributed training

1y exp

“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”

Sri Lalitha

Screened

Senior Full-Stack Java Engineer specializing in cloud-native microservices and FinTech

California, USA6y exp

“Backend engineer who owned a Python task management API with JWT auth, async notifications, and performance work (DB optimization/caching) to handle high volumes. Led an on-prem to Azure private cloud migration at Morgan Stanley using GitOps and IaC (Terraform/ARM) with phased rollout and rollback planning. Also built a Kafka real-time streaming pipeline with exactly-once/idempotent consumers and Prometheus/Grafana monitoring.”

Narendra R

Screened

Senior Full-Stack Java Developer specializing in microservices and cloud platforms

Dallas, TX7y exp

“Backend engineer focused on scalable Python/Flask services and high-performance PostgreSQL/SQLAlchemy systems, with demonstrated wins like reducing N+1-driven response times to under 200ms and cutting P95 latency below 1s via background queues and caching. Has production experience operationalizing ML models as Dockerized APIs on AWS (S3/Lambda) with monitoring (CloudWatch/ELK), plus robust multi-tenant isolation using JWT-driven tenant context and row-level security.”

Utkarsh Chandel

Screened

Senior Security Engineer specializing in detection engineering, cloud security, and DFIR

San Francisco, CA8y exp

“LLM workflow/agentic systems practitioner who has helped customers harden an LLM-based incident triage prototype into a trusted daily-use production system by adding observability, audits, confidence gating, and deterministic fallbacks. Brings an SRE-style approach to real-time debugging (trace replay, rollback/canary, safe toggles) and is experienced running developer-centric demos/workshops and partnering with sales on technical qualification and security/architecture artifacts.”

Meghanath kethireddy

Screened

Mid-level Full-Stack/Backend Engineer specializing in Java microservices and cloud platforms

Dallas, TX5y exp

“PayPal ML/AI practitioner who built and productionized a hybrid recommendation engine (BERT/LLM embeddings + collaborative filtering + XGBoost ranking) on AWS with end-to-end MLOps and orchestration. Addressed real-world issues like cold start and embedding latency (ONNX, clustering, caching, PySpark/Delta Lake) and drove a 27% lift in upsell conversion via A/B testing and stakeholder collaboration with marketing.”

Soham Patil

Screened

Junior Cloud & AI/ML Engineer specializing in AWS GovCloud and MLOps

Washington, DC2y exp

“Robotics software engineer with hands-on ROS 2 autonomy experience on an obstacle-avoiding quadrotor (ROS 2 + Gazebo + PX4 + Nav2/SLAM), including custom work to extend Nav2 into a 3D aerial domain and output PX4 trajectory setpoints. Also built cost-saving ML infrastructure (PostgreSQL + AWS data-cleaning pipeline) and improved object detection accuracy by 40% using CUDA/PyTorch, with strong containerization and CI/CD practices (Docker + Kubernetes, aggressive version pinning) to prevent environment drift.”

Mukundan Sridharan

Screened

Executive Technology Leader (CTO) specializing in IoT sensing, AI/ML, and RF/embedded systems

Rockville, MD22y exp

“Currently a startup CTO who thrives on building new technology stacks and rapidly turning technical ideas into products. Interested in partnering with a CEO/business team to commercialize embedded/edge concepts such as multi-sensor drone localization (video/audio/RF with SDR), low-cost solar+battery power nodes networked via LoRa, and an Amazon Sidewalk/LoRa connectivity device with cloud management.”

Farzam Ebadypour

Screened

Senior Security Sales Engineer specializing in AI security and edge WAF/API protection

San Jose, CA34y exp

“Enterprise customer success / technical sales professional with strong security and fintech account experience, owning onboarding through renewal for a ~5k-employee rollout. Demonstrated measurable outcomes including 80%+ adoption, 3-year renewal with expansion, CSAT 9/10 and NPS 69, plus a reported 60%+ reduction in security analyst review time; experienced driving SIEM/ticketing integrations and influencing product roadmap from customer feedback.”

“Has worked across multiple startup stages from pre-funding through Series D and emphasizes rigorous idea validation through direct conversations with both end users and purchasing decision-makers. Interested in applying NLP to automate summarization/abstracting of highly technical articles, with a balanced view of entrepreneurship that prioritizes health and family.”

Waldorff Villacres

Screened

Healthcare technology executive and architect with 20+ years leading enterprise platforms and digital transformation.

Seattle, Washington, USA21y exp

“Healthcare-focused founder in the R&D stage building an EHR and clinical staffing startup centered on value-based care. They have already tested the concept with the market, are engaging Medicaid/Medicare leaders and industry conferences like ViVE and HIMSS, and are focused on early-signal detection to improve patient outcomes while lowering utilization costs.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”

“Built and shipped a production Python service integrating with a helium mass spectrometer for real-time sensing and remote control/monitoring. Combines hardware integration, observability, and Playwright-based automation expertise, with a strong track record of turning brittle or ambiguous real-world processes into reliable systems— including reducing dashboard automation failures from about 20% to under 2%.”

“Built an internal RAG assistant for financial documents using FastAPI, OpenAI APIs, and vector search, improving document search speed and reducing manual effort for the business team. Stands out for a pragmatic approach to AI engineering: uses AI heavily for productivity, but keeps human judgment central and has designed retrieval, validation, and summarization workflows end-to-end.”

“Engineer working on high-throughput financial systems who uses AI pragmatically to accelerate development without sacrificing design ownership, correctness, or compliance. Particularly interesting for teams building regulated, real-time platforms: they have hands-on experience integrating fraud detection models into microservices, handling transaction ingestion, scoring, decisioning, and throughput-sensitive asynchronous workflows.”

“Senior engineering executive with 8+ years leading large-scale SaaS modernization across AI, compliance, ecommerce, streaming, IoT, and travel. Has led a 150+ global engineering org, modernized seven cloud-native platforms for a $400M business, and consolidated travel systems processing $1B+ annually while staying hands-on in architecture, incident response, and AI integration.”