Vetted Azure Kubernetes Service Professionals

Denvinn Magsino

Screened ReferencesModerate rec.

Mid-level Software Development Engineer in Test specializing in CI/CD and web automation

Los Angeles, CA4y exp

“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.”

“Full-stack engineer with end-to-end ownership across React/TypeScript frontends, Spring Boot/Node microservices, and production ops on Docker/Kubernetes and AWS (ECS/CloudWatch). Built real-time healthcare eligibility and analytics systems at Cigna and an early-stage seller onboarding platform at Flipkart, driving measurable performance gains (35–40% latency/throughput improvements) through event-driven Kafka pipelines, Redis caching, and strong reliability/observability practices.”

“Cloud-focused engineer experienced deploying and stabilizing complex production systems that span APIs, infrastructure, and automated workflows, with a strong observability and safe-release mindset (feature flags/canaries/rollbacks). Has hands-on, customer-facing incident leadership, including executing DR regional failover during an AWS us-east-1 outage to maintain service and reportedly save a client ~$10M.”

“LLM/agent workflow engineer with healthcare experience (CVS/CBS Health) who built and deployed a production call-insights platform using Azure OpenAI + LangChain/LangGraph, including sentiment and compliance checks. Demonstrates deep HIPAA/PHI handling (tenant-contained processing, redaction, RBAC/encryption/audit logging) and production rigor (testing, eval sets, validation/retries, autoscaling) to scale to thousands of transcripts.”

“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.”

“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”

“Software engineer with strong compliance-domain experience who built a customer-facing compliance and reporting dashboard using React/TypeScript with Spring Boot microservices. Demonstrates mature production engineering practices—contract-first APIs, event-driven architecture (Kafka/RabbitMQ), caching (Redis), and robust CI/CD + observability (Prometheus/Grafana/ELK)—and also created a Python-based audit automation tool adopted into the standard release process.”

“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.”

“Software engineer at Wrap who built production AWS Lambda services for large-scale Parquet dataset generation (50k+ records) and a synthetic traffic/lead generation system using Python, Playwright, and Jenkins. Also built and deployed a full-stack hobby product (MyAnimeListRanker) that ingests MyAnimeList user data and uses an Elo-based ranking workflow, with operational guardrails like rate limiting and monitoring via Vercel/logs.”

Ritvik Nimmagadda

Screened

Junior AI/ML Software Engineer specializing in LLMs and MLOps

Remote3y exp

“Built and productionized an AI-native, agentic appeals decisioning system for health insurance operations, automating 500k+ scanned appeals/year. Delivered measurable impact by cutting review time from 12–15 minutes to ~3 minutes and auto-resolving ~85% of cases with strong auditability, evaluations, and human-in-the-loop guardrails, deployed as containerized microservices on Azure AKS.”

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.”

“Backend engineer with hands-on experience building event-driven Java/Spring Boot and Kafka systems, plus AI-assisted document-classification workflows in enterprise environments. Stands out for a thoughtful, risk-aware approach to AI: uses it to accelerate delivery, but emphasizes validation layers, confidence thresholds, observability, and human review before AI can affect downstream business actions.”

“Software engineer with Intuit experience shipping an end-to-end real-time financial insights product on AWS, using event-driven architecture with Kafka and Spark Streaming to process millions of records with low latency. Also delivers customer-facing React + TypeScript dashboards and has hands-on production operations experience, including resolving a database scaling incident via read replicas, query tuning, and connection pooling.”

“Built and shipped a production LLM-powered personalized client engagement assistant in the financial domain, balancing real-time recommendations with strict privacy/compliance requirements. Demonstrates strong MLOps/LLMOps depth (Airflow + MLflow, containerized microservices, drift monitoring) and a privacy-by-design approach validated in collaboration with risk and compliance teams.”

“Enterprise platform engineer/product owner who led end-to-end delivery of customer-facing ServiceNow Service Catalog/workflow solutions, emphasizing reliability, security, and fast iteration. Built React/TypeScript portals with Node.js and Spring Boot backends, and improved microservices reliability at scale using Kafka, monitoring, and robust retry/timeout patterns.”

“GenAI/data engineering practitioner with production experience across Equinix, Optum, and Citibank—built an Azure OpenAI (GPT-4) + LangChain document intelligence platform processing 1.5M+ docs/month and a HIPAA-compliant Airflow healthcare pipeline handling 5M+ claims/day. Also delivered a real-time fraud detection + explainability system using LightGBM and a fine-tuned T5 NLG component, improving fraud accuracy by 15%+ while partnering closely with compliance stakeholders.”

Deepthi Pamisetty

Screened

Mid-level Full-Stack Engineer specializing in FinTech and cloud-native systems

Dallas, TX6y exp

“Full-stack engineer with production experience building AI-powered search and automation systems at JPMorgan Chase and customer-facing product features at Wayfair. Stands out for combining React frontend work with backend microservices, RAG/LangChain AI integration, and cloud-scale performance tuning, including a support chatbot that reduced ticket resolution time by 35%.”

“IBM full-stack engineer focused on document automation for internal and government use cases, building Java Spring Boot services and React/TypeScript UIs that automate XML-to-multilingual PDF workflows. Stands out for turning complex backend automation into usable tools for non-technical operations teams while improving processing speed and reducing manual effort.”

“Built and shipped AI-powered fraud investigation and risk dashboard features at Barclays, combining full-stack engineering with RAG-based LLM systems in a compliance-sensitive environment. Stands out for pairing explainable AI architecture with measurable business impact: improving fraud detection accuracy by 18% while reducing analyst investigation time by 14%.”

“Built and owned major parts of a real-time distributed AI fraud-detection pipeline (ingestion, inference microservice integration, and automated action layer), optimizing latency and observability and reducing false positives by ~35%. Understands ROS/ROS2 concepts (nodes/topics/services) and planned hands-on ramp-up via ROS2 pub/sub exercises and Gazebo simulation, but has not worked on physical robots or ROS in production.”

“Platform-focused engineer experienced in productionizing ML/LLM systems: containerized a local prototype, implemented CI/CD, deployed to Kubernetes with scaling controls, and added monitoring/logging. Comfortable diagnosing real-time issues in LLM/agent workflows using logs/metrics and incident stabilization tactics, and supports sales calls by clearly explaining production scalability to unblock customer decisions.”