Vetted Azure DevOps Professionals

“QA automation engineer with ad-tech domain experience (Prebid.js wrapper-based services) who built an end-to-end Python automation framework using Playwright to validate wrapper settings, auction request/response, and analytics payloads. Uses Jenkins-driven CI reporting and feature-categorized regression runs to quickly isolate revenue-impacting defects and coordinate fast fixes with developers.”

“LLM/agentic systems practitioner who specializes in moving demo-only assistants into reliable, observable, cost-controlled production services. Strong in real-time diagnosis of complex agent workflows (including tracing, loop detection, and guardrails) and in customer-facing enablement—running workshops, building tailored PoCs, and partnering with sales to close deals by proving reliability in high-risk pilots.”

“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.”

“Built a production multi-agent recommendation/RAG system for internal data analysts to speed up weekly report creation by improving document discovery and automating report/SQL generation. Implemented LangGraph-based orchestration with deterministic agent routing, robust error handling (interrupt/resume), and metadata-driven semantic chunking for diverse PDF/document formats, plus monitoring for latency, throughput, and token/cost efficiency.”

“Full-stack engineer who has owned customer-facing AI recommendation and analytics dashboards end-to-end (backend APIs/data processing through React UI, deployment, and monitoring). Demonstrates strong systems thinking around scaling microservices—using observability, caching, async workflows, and resilience patterns—and also built an internal ops dashboard that became the default tool for on-call incident reviews.”

“Backend-focused engineer who built a resume processing and job application platform using Python/MongoDB/Streamlit, including OpenAI-powered skill/keyword extraction and recruiter-facing search/filtering. Has hands-on cloud deployment experience on AWS/Azure and executed an on-prem reservation portal migration to Azure using a phased trial-and-cutover approach; also automated CI/CD with Jenkins and GitHub Actions.”

“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”

“ML/NLP engineer currently working in NYC on a system that connects complex unstructured data sources to deliver personalized insights, using embeddings + vector DB retrieval and a RAG architecture (LangChain, Pinecone/OpenSearch). Strong focus on production constraints—especially low-latency retrieval—using FAISS/ANN, PCA, index partitioning, and Redis caching, plus PEFT fine-tuning (LoRA/QLoRA) and KPI/SLA-driven promotion to production.”

“Built a production RAG-based GenAI copilot backend at Aetna using Python/FastAPI, GPT-4, LangChain, and Azure AI Search, deployed on AKS with Prometheus/Grafana observability. Owned the system end-to-end (ingestion through deployment) and improved peak-time reliability by addressing vector search and embedding bottlenecks with Redis caching, index optimization, and async processing, plus added anti-hallucination guardrails via retrieval confidence thresholds.”

“iOS player-coach who led end-to-end development of real-time customer support chat and unified notification systems for T-Mobile’s iOS app using SwiftUI, Firebase, WebSockets, and Core Data (including offline handling). Drove measurable reliability/latency gains (~30%) through a major notification refactor and owned a high-severity push-notification incident from rollback through RCA and backward-compatible hotfix, while also scaling team process and people management.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“LLM/RAG engineer who has built and deployed corporate-scale systems at Novartis and Johnson & Johnson, including a healthcare AI agent that generates day-to-day treatment schedules. Recently handled a high-stakes safety incident (LLM suggesting overdose) by tightening model instructions and validating with ~200 test prompts, and has strong end-to-end data/embedding/vector DB pipeline experience (PySpark, FAISS, Pinecone) plus SME-in-the-loop evaluation (RLHF).”

“Software engineer with deep experience in real-time payments and event-driven microservices. Built a React/TypeScript + Spring Boot system using RabbitMQ, and created an internal operations dashboard that improved visibility into message-processing workflows for engineering, support, and SRE. Strong in experimentation-driven product iteration (feature flags/A-B tests) and in scaling reliability via idempotent consumers and end-to-end observability.”

“Built and productionized an NLP-powered customer support assistant at JPMorgan Chase for digital banking, focused on reducing response time for repetitive client queries. Strong in real-world AI deployment challenges—sensitive data handling, low-latency FastAPI services, and AWS/Kubernetes operations with CI/CD—plus a metrics- and guardrails-driven approach to reliable AI workflows.”