Vetted Kubernetes Professionals

“Built and deployed a production LLM/RAG document automation system for business documents (contracts/claim forms) that extracts schema-validated JSON, generates grounded summaries/Q&A, and integrates into transaction systems via APIs. Emphasizes real-world reliability: hallucination controls, layout-aware parsing with OCR fallback, Step Functions-orchestrated workflows with retries/timeouts, and human-in-the-loop review designed in close partnership with operations and claims stakeholders.”

“Former founder who built a revenue-generating DevOps GTM service from zero, using milestone-based revenue targets and multi-channel selling (relationships, channel partners, and major conferences like AWS events and Dreamforce). Also led a cross-functional FedRAMP Moderate readiness strategy to enable selling into regulated environments, coordinating engineering/product/finance/sales/security/support and third-party partners under a tight timeline.”

“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.”

“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.”

“Amazon engineer who built a production AI agent platform (Python/AWS Strands on Bedrock) that lets teams create tool-using, multi-agent workflows—e.g., agents that auto-triage and resolve customer support tickets by reading internal documentation and collaborating with a research agent. Previously worked in Deloitte on IAM using Ping Identity/Ping DaVinci orchestration, and applies orchestration thinking plus structured evaluation (LLM-as-judge, surveys, automated tests) to improve agent reliability.”

“AI/ML full-stack practitioner in a small-scale manufacturing/lab operations environment who deployed a production ML system to improve blood cell order fulfillment by predicting yield/success from donor characteristics. Experienced building custom multi-agent orchestration (Python, LangChain/LangGraph, MCP) and balancing reliability, data quality constraints, and token/ROI economics while communicating tradeoffs to VP-level business stakeholders.”

“Full-stack developer (Energywell) who led an internal admin dashboard end-to-end using React/Redux and a Go microservice, emphasizing performance (reduced calls, preload data) and maintainable architecture (modularity, refactoring, PR reviews). Also shipped a Redis-based caching whitelist feature in a fast-paced environment and helped implement a responsive, brand-configurable onboarding/signup frontend.”

“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.”

“Backend engineer focused on Python/FastAPI microservices, with hands-on experience deploying to AWS (EKS/ECR) via Jenkins and GitOps-style workflows using ArgoCD. Has built and stabilized real-time Kafka payment-event streaming pipelines and improved production performance under peak load through Redis caching, SQL optimization, and robust retry/DLQ patterns. Also supported phased migrations from on-prem environments to AWS with gradual traffic shifting and monitoring.”

“Built and productionized an autonomous research agent (AutoGPT) in a Docker/Kubernetes environment with Pinecone-based long-term memory and custom Python tools for analysis, visualization, and report drafting. Implemented layered guardrails (prompt templates, automated validation, self-critique loops, and monitoring) and achieved ~25% reduction in manual report generation time while scaling the workflow to support multiple concurrent users.”

“Frontend-focused engineer with experience building and optimizing React-based dashboards and reusable component libraries in a multi-team, internal open-source-style environment at Merck (ClearSight Forecasting Dashboard). Also handled production user issues on a live streaming platform (GameSee.tv) and built a financial application from scratch at Manipal Business Solutions, owning backend services, middle-tier APIs, and third-party integrations.”

“Built and deployed a production LLM + vector search clinical decision support system at UnitedHealth Group, retrieving medical evidence and patient context in real time for prior authorization and risk scoring. Strong in end-to-end RAG architecture (Hugging Face embeddings, Pinecone/FAISS, SageMaker, Redis) plus orchestration (Airflow/Kubeflow) and rigorous evaluation/monitoring, with demonstrated ability to align solutions with clinical operations stakeholders.”

“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.”

“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.”

“AI/ML engineer with HP experience building and productionizing an LLM-powered document intelligence platform (LangChain + Pinecone) to deliver semantic search and contextual Q&A across millions of enterprise support documents. Demonstrates strong MLOps and scaling expertise (Airflow, Kubernetes autoscaling, Triton GPU inference, monitoring with Prometheus/W&B) plus a structured approach to evaluation (A/B tests, shadow deployments, failover) and effective collaboration with non-technical stakeholders.”

“Full-stack engineer (backend-led) with recent experience building enterprise workflow orchestration and billing/payment platforms at Intuit using Java/Spring Boot (WebFlux), Kafka, Postgres/Redis, and React/TypeScript. Has operated at high scale (reported ~1200 RPS during month-end billing) and focuses on event-driven microservices, real-time UI updates via streaming, and disciplined API evolution with contract testing.”

“Backend/platform engineer with payments domain experience, having owned core services for MasterCard’s global card tokenization and settlement platform. Built Django/Celery microservices plus Kafka/Redis real-time fraud streaming, delivering 27% latency improvement, sub-100ms fraud checks, and 18% fewer false positives. Strong DevOps/IaC background across Kubernetes, AWS ECS, Terraform, GitHub Actions, and GitOps practices for high-scale transaction systems (including UPI at PhonePe).”

“Backend engineer with experience at Comcast and in healthcare/pharmacy automation (PrimeRx), building Python/Flask services that orchestrate large-scale batch workflows (Airflow) and high-throughput event processing (Kafka). Demonstrated measurable performance wins (cut provisioning latency to ~150–200ms) and strong multi-tenant isolation strategies (Postgres RLS, partitioning), plus practical integration of ML model outputs into production systems with validation and fallback controls.”

“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.”

“Internship experience building a production RAG+LLM pipeline to map messy card transaction descriptions to merchant brands, including a custom modified-ROUGE evaluation approach for weak/variant ground truth. Improved scalability and cost by moving from a managed LLM endpoint (e.g., Bedrock) to self-hosted vLLM, and orchestrated massive embedding backfills (5,000+ files, 10B+ rows) using an Airflow-triggered SQS + ECS worker architecture with robust retry/DLQ handling.”

“Built a lightweight internal JavaScript analytics tracker capturing user interactions (clicks, page views, custom events) with debounced batching, automatic session tracking, and offline event caching via a localStorage-backed append-only queue. Demonstrates practical performance optimization mindset (profiling, memoization/caching) and React performance tuning.”

“Backend/platform engineer from Fanatics sportsbook core team with deep experience in real-time ingestion systems (Kafka) and high-throughput performance optimization. Delivered an 87% latency reduction on a Java API handling hundreds of thousands of updates per second, and improved reliability of shared internal libraries via deterministic recovery logic, strong testing, and feature-flagged rollouts.”

“Engineering manager with recent hands-on technical leadership in vendor-based geo augmentation, including making a key pivot from a broken vendor SDK to an internal data ingestion approach. Previously shipped impactful Python microservice refactors that reduced unnecessary data processing/storage and improved runtime payload efficiency, and has owned on-call incidents through mitigation (scaling pods) and prevention process changes.”

“Backend/AI engineer who built a real-time vector database system for high-frequency financial data using Kafka/Flink on Kubernetes, achieving sub-100ms similarity search at 10k+ concurrent load and resolving tricky duplication issues with idempotency/versioning. Also shipped an end-to-end LLM-based travel itinerary feature (profiling + prompt workflows + APIs) with a focus on quality consistency and low latency.”