Vetted Azure Blob Storage Professionals

“Backend/full-stack engineer who has built AI-powered search and workflow systems in production, including a semantic resume-matching platform for recruiters and internal security data dashboards at ReliaQuest. Stands out for combining modern AI tooling with pragmatic reliability, performance tuning, and strong product intuition in ambiguous environments.”

“Open-source JavaScript library contributor/maintainer focused on performance and usability—uses profiling and user feedback to optimize large-dataset processing and modernize abstractions. Refactored a nested-callback event handling system into an observer-pattern dispatcher with batched event queues, reducing CPU usage and improving maintainability; also handles community-reported crashes by reproducing issues, fixing memory leaks, and updating docs.”

“Backend/ML platform engineer in the insurance domain who built and shipped an AI-driven risk scoring/fraud detection service for underwriting. Runs containerized .NET Core and Python inference services on Azure (AKS + GPU nodes) with Terraform/ARM and Azure DevOps CI/CD, and has hands-on experience improving reliability under peak load plus implementing production AI guardrails (drift monitoring, fallbacks, human review, audit logs).”

“Backend engineer (Marsh McLennan) who evolved a high-volume claims automation pipeline in Python, emphasizing thin APIs with background job processing, strong validation/retries, and production-grade observability. Experienced in secure FastAPI API design (centralized JWT/RBAC), multi-tenant Postgres/Supabase-style row-level security, and low-risk refactors using parallel runs and feature flags; targeting founding-engineer scope roles.”

“Data engineer in a banking context who has owned end-to-end Azure lakehouse pipelines ingesting financial/vendor data from APIs, Azure SQL, and flat files into Databricks/Delta (bronze-silver-gold). Emphasizes production reliability via schema-drift validation, data quality controls, monitoring/alerting, retries/checkpointing, and Spark/Delta performance tuning, with outputs served to BI/reporting teams (e.g., Tableau).”

“Data-focused engineer who has built near real-time trending news sentiment pipelines end-to-end (API/web ingestion, validation, transformations, and dashboard serving) and implemented reliability patterns like retries with exponential backoff and backfills. Also shipped Java/Spring Boot REST APIs backed by SQL with indexing/pagination, and stood up an early-stage QR-based attendance MVP using Firebase with iterative hardening via logging and validation.”

“Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.”

“Full-stack engineer with end-to-end ownership of a production customer plan activation and account management flow at T-Mobile, spanning Java/Spring Boot APIs, React frontend, and Docker-based CI/CD deployments. Demonstrated performance/scalability work (query optimization, indexing, caching) and measured success via improved retrieval speed and reduced support tickets.”

Harika M

Screened

Mid-level Full-Stack Developer specializing in FinTech and enterprise platforms

Plano, TX6y exp

“Engineer with a pragmatic, production-focused approach to AI-assisted development, using tools like Copilot and ChatGPT to accelerate coding while maintaining strict validation for correctness, security, and performance. Particularly notable for building a multi-agent incident-resolution workflow for a financial platform, with specialized agents for log analysis, root cause identification, fix suggestions, and test generation.”

“Robotics software/ML engineer who built perception and navigation-related ML systems for autonomous supermarket carts, including object detection, shelf recognition, and obstacle avoidance. Strong ROS/ROS2 practitioner who optimized real-time performance (reported 50% latency reduction) and deployed containerized ROS/ML pipelines at scale using Docker, Kubernetes, and CI/CD.”

“Backend/data platform engineer who has built and operated a cloud-native media ingestion/processing platform in Python (Django/DRF, FastAPI) with Kafka, Postgres, and Redis, emphasizing multi-tenant security and reliability. Delivered AWS production systems combining EKS and Lambda with Terraform + GitHub Actions/Helm, and built Glue-based ETL pipelines with strong schema-evolution and data-quality practices; also modernized SAS analytics into Python on AWS. Seeking fully remote roles with a $120K–$140K base range.”

“GenAI engineer who has deployed production LLM/RAG chatbots for internal document search, focusing on reliability (hallucination reduction via prompt guardrails + retrieval filtering) and performance (latency improvements via caching). Experienced with LangChain/LangGraph orchestration for multi-step agent workflows and iterates using monitoring/logs and benchmark-driven evaluation while partnering closely with product and business teams.”

“Software engineer with deep healthcare claims domain experience who has owned customer-facing portals end-to-end (Java/Spring Boot + React/TypeScript) and improved usability/performance based on real user feedback. Built microservices using REST and RabbitMQ with strong observability (Splunk/cloud metrics), and delivered an internal claims investigation dashboard that streamlined operations through centralized data, search, and filtering.”

“Financial data engineer who has built and operated high-volume batch + streaming pipelines (200–300 GB/day; 5–10k events/sec) using AWS, Spark/Delta, Airflow, Kafka, and Snowflake, with strong emphasis on data quality and reliability. Demonstrated measurable impact via 99.9% SLA adherence, major reductions in bad records/nulls, MTTR improvements, and significant latency/runtime/query performance gains; also built a distributed web-scraping system processing 5–10M records/day with anti-bot and schema-drift defenses.”

Sreeja Venkatakrishnan

Screened

Mid-level Full-Stack Developer specializing in Java/Spring and modern JavaScript frameworks

Virginia, USA4y exp

“Full-stack engineer with hands-on experience building real-time applications (Socket.io chat app) and data-heavy systems in banking/loan management. Comfortable across React and backend services (Spring Boot/Node), with a focus on scalable API design, database performance (indexing/pagination/caching), and deployment via CI/CD and cloud infrastructure.”

Nagaveda Sai Kumar Reddy Gajjala

Screened

Mid-Level Full-Stack Software Engineer specializing in distributed systems and cloud integrations

Richardson, TX4y exp

“Backend engineer with enterprise SaaS experience (Zoho) who owned an end-to-end cloud integration between Endpoint Central and ServiceDesk Plus, redesigning device onboarding across 64+ scenarios and building a fault-tolerant sync engine that recovered 100% failed transactions. Also built and operated production systems across the stack—FastAPI services with strong testing/observability, React+TypeScript portals, PostgreSQL performance tuning, and AWS deployments with real incident response (RDS CPU saturation resolved with zero downtime).”

“Senior data scientist with ~5 years’ experience building production ML/NLP systems in finance (Wells Fargo) and deep learning for sensor analytics in connected vehicles (Medtronic). Has delivered end-to-end platforms combining time-series forecasting with transformer-based NLP, including automated drift monitoring/retraining (MLflow + Airflow) and standardized Docker/CI/CD deployments; achieved a reported 22% precision improvement after domain fine-tuning.”

“Backend/ML systems engineer who built a Flask + PostgreSQL internal ticketing platform and demonstrates strong database/ORM performance depth (indexes, partitioning, RLS multi-tenancy). Notably optimized a high-throughput attachment OCR/embedding pipeline with batching, deduplication, and Redis caching, cutting median latency from 45s to 10s and reducing worker cost by 35% while increasing throughput 4x.”

“Full-stack Python engineer who has owned internal automation products from requirements through production, including a financial reporting platform that improved deployment time by 45% and raised reporting efficiency to 98%. Also built an AI-powered movie recommendation engine using collaborative and content-based filtering, with hands-on experience across frontend, backend, data pipelines, and ML evaluation.”

“Full-stack engineer focused on productionizing LLM applications, including an Android privacy-policy risk summarization app (Kotlin/React Native + FastAPI + Ollama) that cut response times from ~10s to ~5–6s via batching, caching, async, and event-driven architecture. Currently at PRGX building an LLM-based legal contract clause extraction system, partnering closely with legal/procurement SMEs to create schemas, labeled datasets, and evaluation pipelines that improved accuracy from 70% to 85%. Also has experience architecting real-time voice/LLM systems with streaming microservices (Kafka, Kubernetes, gRPC/WebSockets) and an avatar chatbot pipeline (TalkingHead, Google TTS, AnythingLLM).”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

Aniruth Ravula

Screened

Mid-level Full-Stack Java Developer specializing in Angular and Spring Boot microservices

5y exp

“Full stack Java developer (5 years Java/Spring Boot) building a mortgage-focused rule engine platform used by business users and developers. Experienced scaling data-intensive microservices on AWS (RDS/S3/SQS) and optimizing high-volume rule processing with SQL tuning, caching (KIE container), and asynchronous task decoupling; also delivers modern UIs in Angular and React (Redux/Toolkit).”

“Built end-to-end product features spanning full-stack web development and LLM-powered systems in an early-stage startup environment. Notably shipped an AI financial assistant chatbot with agent routing, validation, fallback handling, and production monitoring, and also owned a scheduling system integrating Next.js, backend APIs, database design, and Google Calendar OAuth.”