Vetted Azure Blob Storage Professionals

“Infrastructure/DevOps-focused engineer working across Linux-based enterprise platforms that include IBM Power/AIX in a broader OpenShift/Kubernetes and cloud ecosystem. Built Azure DevOps CI/CD for containerized deployments and resolved a production deployment failure by tracing ImagePullBackOff to outdated registry credentials in Kubernetes secrets. Uses Terraform (with modular structure) plus Ansible to provision and standardize production environments with pipeline-based validation.”

“Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.”

“Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.”

“Data platform engineer with hands-on ownership of high-volume financial data pipelines (millions of transactions/day) on Azure (ADF, Databricks, Delta Lake, Synapse), emphasizing schema-drift protection and automated data-quality gates. Also built resilient web scraping pipelines with anti-bot and backfill strategies, and shipped a versioned FastAPI + Redis data API with autoscaling, testing, and CI/CD via GitHub Actions.”

Mounika Gunturu

Screened

Senior Python Full-Stack Developer specializing in cloud-native microservices and data platforms

New York, NY9y exp

“Backend/data engineer from Oliver Wyman who built and ran production Python (FastAPI) services on AWS (ECS/Lambda/API Gateway) supporting risk modeling and regulatory reporting. Strong in reliability/observability, Glue-based ETL with data quality controls, and legacy SAS-to-Python modernization with rigorous parity validation; also demonstrated measurable SQL performance wins and cost-control improvements in serverless scaling. Based in Raleigh, NC and can travel onsite for important Bethesda-area meetings.”

Jahnavi Bellapukonda

Screened

Mid-Level Software Engineer specializing in full-stack web and cloud systems

Remote, USA3y exp

“Full-stack engineer with strong data engineering and privacy-domain experience, having owned an automated Data Subject Rights (DSR) processing pipeline end-to-end across Azure SQL and GCP (GCS/BigQuery). Emphasizes production reliability via idempotency, validation checkpoints, structured logging/monitoring, and safe CI/CD-driven deployments, and has also built React+TypeScript + Node/Postgres web apps with scalable, maintainable architecture.”

“Backend/ML engineer who built production LLM-enabled systems at PRGX, including an interpretable contract opportunity scoring engine (Bradley-Terry pairwise ranking) that reached 0.82 weighted Spearman agreement with SME auditors and was integrated into workflow. Also built a Duke student advisor chatbot and hardened it for real-world reliability/security with schema-driven tool calling, normalization, and off-domain defenses; led staged production rollouts with shadow testing and achieved 0.90 F1 on a new extraction field before shipping.”

“Built and deployed an LLM-powered analytics assistant enabling business users to ask questions in plain English and receive validated Spark SQL executed in Databricks, with a Streamlit/Flask UI. Addressed strict client schema-privacy constraints by implementing a RAG strategy and ultimately leveraging AWS Bedrock and fine-tuned reference docs. Also has production ML pipeline experience using Docker + Airflow and AWS (S3/ECS/EC2) for financial classification models.”

“Backend/data engineer with experience at Citi and Elevance Health, building end-to-end pipelines and data services in regulated, high-volume environments. They combine Python, SQL, .NET, Azure Functions, and strong observability/reliability patterns to improve processing speed, reduce manual effort, and maintain high uptime across financial and healthcare data platforms.”

Bhanu Prakash Reddy Dakilli

Screened

Mid-level Data Engineer specializing in Azure ETL/ELT and data warehousing

Framingham, MA4y exp

“Data engineer who has owned end-to-end production pipelines for customer transaction data (~2–5 GB/day) using Python/PySpark/SQL and Airflow, delivering major reliability and speed gains (70% faster reporting; 60–70% fewer data issues). Also built a daily external web-scraping system with anti-bot handling and safe, idempotent Airflow-driven backfills, plus a Python data API optimized with indexing/caching and tested for correctness.”

“Data platform engineer focused on large-scale, real-time fraud systems, with hands-on ownership of streaming architectures using Kafka, Spark, Snowflake, and Databricks. Stands out for combining performance tuning and platform automation with LLM/RAG-based enrichment, delivering measurable gains in latency, fraud accuracy, false positives, and analyst decision speed.”

“Platform-focused engineer/technical program leader working in silicon/wafer validation environments, with hands-on experience securing access to sensitive test results and engineering tooling. Has implemented RBAC/least-privilege controls with Azure Entra ID, Key Vault, PAM and integrated Checkmarx into dev workflows, while also deploying ML services on AKS using Bicep/Helm/Docker and Azure DevOps CI/CD with strong monitoring and incident response practices.”

“Full-stack engineer with fintech/trading domain experience (Fidelity) and startup SaaS CRM/billing platform work (Zoho), building real-time portfolio analytics and trade-processing systems. Strong in microservices, event-driven architectures (Kafka/WebSockets), and AWS/Kubernetes operations with measurable performance gains (~34–35% latency reduction) and maintainability improvements (~40% faster deployments). Targeting a founding full-stack engineer role in NYC with meaningful equity.”

“Built GoEngineer’s first production AI systems, including an end-to-end RAG pipeline for SolidWorks technical support using Azure Blob Storage, Azure AI Search, and Azure OpenAI, plus an AI summarization feature adopted by sales/customer success. Strong in productionizing LLM workflows with evaluation harnesses (golden sets, LLM-as-judge, red teaming, shadow deploys) and Azure infrastructure integrations (Redis, Service Bus, App Insights), and has also implemented a custom MCP server for agentic monitoring.”