Vetted Confluence Professionals

“Enterprise IT operations and identity governance professional (Atlassian) who partners closely with security teams to reduce risk through RBAC/Conditional Access automation, endpoint compliance, and vulnerability remediation workflows. Demonstrated ability to balance strict security controls with engineering productivity via phased rollouts, pilots, and KPI-driven stakeholder alignment, plus hands-on troubleshooting of complex Azure AD authentication failures and secure AWS integration design.”

“Full-stack Java developer with IBM and Epic Systems experience modernizing legacy enterprise apps into microservices and delivering customer-facing healthcare claims workflows at very high scale (2M+ transactions/day). Strong blend of product engineering (APIs + React/TypeScript UI) and production operations on AWS, including performance incident remediation via query optimization, indexing, and autoscaling.”

“QA Automation Engineer with 7+ years building dependable enterprise automation suites across UI, API, and database layers using Selenium (Java), Playwright, Karate, and Cypress. Integrates smoke/regression suites into CI/CD (GitLab/Jenkins/GitHub Actions) with reporting and notifications, and has prevented production issues by catching silent backend failures and high-impact payment defects through end-to-end validation and strong root-cause evidence.”

“Built and deployed a production compliance automation RAG system (at Citi) that generates citation-backed, schema-validated risk summaries for regulatory document review. Emphasizes regulated-environment reliability with retrieval-only grounding, abstention, confidence thresholds, and immutable audit logging, plus orchestration using LangChain/LangGraph and Airflow. Reported ~60% reduction in compliance review effort while maintaining high precision and traceability.”

“QA professional from enterprise SaaS/cloud environments (Exeter; previously Boeing programs) with strong UI and end-to-end workflow testing experience. Known for rigorous defect investigation and validation across UI/API/DB layers (including SQL checks) and disciplined tooling/workflow in Jira/Azure DevOps/ALM Octane; has QA lead experience supporting release readiness and go/no-go decisions. No professional game studio/AAA shipping experience yet, but experience maps to complex, high-pressure release cycles.”

“Backend/platform engineer who has owned end-to-end production systems in financial/claims domains, including a transaction analytics microservice platform processing ~10M daily operations and cutting latency from ~150ms to <70ms. Also productionized an LLM-powered monitoring/alerting capability (Llama 3 + FastAPI) with prompt design, guardrails, and production evaluation, and led monolith-to-microservices modernization on AWS using feature flags and parallel runs.”

“Built and productionized AI-driven insurance claims document intelligence/automation at American Family Insurance, integrating OCR/NLP models and a rules-based validation layer into existing claims systems via APIs. Delivered measurable impact (≈28% accuracy lift, ≈35% reduction in manual processing time) and modernized legacy workflows with phased cloud migration, feature flags, parallel runs, and CloudWatch-based monitoring.”

“FDE at Bland.ai and founder of Fi (incident-response agent) who routinely takes LLM/agentic concepts from prototype to production. Has hands-on experience reverse-engineering undocumented systems to deliver integrations, building LLM testbeds for voice-agent reliability, and rapidly shipping RAG/semantic search solutions (e.g., Confluence runbooks) after deep customer discovery with DevOps/SRE teams.”

“Backend engineer with TaskRabbit experience building and operating payment/booking services in Python/Django on AWS (ECS + Lambda) with Kafka/SQS eventing. Demonstrates strong production reliability and incident ownership in high-stakes payment flows (idempotency, strict timeouts, retries, monitoring/alerting) plus data/ETL work in AWS Glue and measurable SQL performance wins.”

“Full-stack engineer who built an internal user-activity tracking and reporting system end-to-end using React/TypeScript, Node/Express, and Postgres, deployed on AWS (EC2/ALB, S3/CloudFront) with CloudWatch observability. Emphasizes reliability and data correctness via idempotent ingestion, retries with exponential backoff, backfills/reconciliation, and performance tuning as data scales, and has experience shipping quickly in ambiguous early-stage startup conditions.”

“LLM/ML platform engineer with hands-on experience taking an LLM document summarization prototype into a production-grade service on AWS EKS, emphasizing low-latency inference, drift monitoring, and safe CI/CD rollouts (canary + rollback). Strong in real-time debugging of agentic/RAG systems (tracing, retrieval/index drift fixes) and in developer enablement through practical workshops (Docker/Kubernetes/FastAPI) plus pre-sales support via demos and benchmarks to close pilots.”

“Built and deployed production LLM-powered document summarization/classification systems using embeddings, vector databases (RAG-style retrieval), and automated evaluation (BERTScore/ROUGE), with a focus on monitoring and scalable cloud pipelines. Also partnered with a fraud analytics team to deliver a transaction anomaly detection solution, translating model outputs into Power BI dashboards and actionable KPIs while iterating on thresholds and alerts based on stakeholder feedback.”

“Embodied AI / robotics-focused ML engineer with experience at JPMorgan and EY building language-to-robot control systems that connect transformer/LLM intent to safe real-world robotic actions. Designed production-grade, low-latency architectures (Kafka/Redis, monitoring, CI/CD) and applied sim-to-real and model distillation to make research ideas deployable on physical systems.”

“Built and productionized an LLM-powered clinical documentation and insights pipeline at Cardinal Health using LangChain + GPT-4 with RAG to summarize long clinical notes, extract medication/dosage entities, and generate structured SQL-ready outputs for downstream analytics. Emphasizes clinical reliability via labeled benchmarking (precision/recall/F1), shadow deployments, clinician human-in-the-loop review, and ongoing monitoring/orchestration with Airflow, Lambda, S3, Postgres, and Power BI.”

“PayPal ML/AI practitioner who built and productionized a hybrid recommendation engine (BERT/LLM embeddings + collaborative filtering + XGBoost ranking) on AWS with end-to-end MLOps and orchestration. Addressed real-world issues like cold start and embedding latency (ONNX, clustering, caching, PySpark/Delta Lake) and drove a 27% lift in upsell conversion via A/B testing and stakeholder collaboration with marketing.”

“Game QA tester/designer hybrid with hands-on Unreal Engine 5 technical debugging experience, focused on NPC AI navigation and performance. At Lantern Tower Games (Telmahre), owned end-to-end quality for an NPC system (dynamic navmesh, moving platforms, EQS, actor pooling), using Unreal Insights and asset reference refactors (hard->soft + async loading) to resolve critical memory/performance issues while collaborating closely with engineering and level design in Scrum.”

“Marketing/paid social creative lead who recently ran a 3-month campaign for the Shortzy! app, owning strategy through QA while coordinating freelancers on a small budget. Combines entertainment/on-set direction experience with performance marketing fundamentals (A/B testing, CPA/ROAS/CTR) from Google Marketing certification work, plus conversion assets like landing pages and subscription messaging.”

“Backend/DevOps engineer pivoting into robotics/space, building hands-on ROS2 (Humble) skills via Gazebo simulations and experimenting with Nav2 and slam_toolbox. Brings strong distributed-systems and real-time debugging practices (profiling, instrumentation, QoS/retry patterns) and is actively learning perception and control fundamentals to transition into autonomous robotics.”

“Frontend-focused engineer with experience at T-Mobile and U.S. Bank who maintained a TypeScript utility library (types, tests, build pipeline, and docs) adopted by multiple teams, and improved React workflow performance by refactoring components and optimizing data fetching. Known for pragmatic cross-team support—reproducing issues quickly, shipping well-tested fixes, and managing changes carefully to avoid breaking downstream apps.”

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“ML/GenAI engineer with production experience building an LLM-powered governance layer that summarizes verified drift/performance signals into validation reports and release notes, designed for regulated environments with de-identification and non-blocking fallbacks. Strong Airflow-based orchestration background across healthcare and finance, integrating Databricks/Spark and MLflow for scalable retraining/monitoring. Demonstrated ability to partner with non-technical healthcare operations teams to deliver actionable risk-scoring outputs via dashboards and automated reporting.”

“Backend engineer who built a containerized Flask service powering an engineering metrics dashboard by syncing GitHub and Jira data into PostgreSQL, with strong emphasis on schema design, query performance, caching, and background processing. Has hands-on experience with SaaS multi-tenancy (tenant scoping + Postgres RLS) and integrating AI/ML inference via separate model-serving services (FastAPI + TensorFlow Serving) and external APIs (OpenAI/Hugging Face/PyTorch).”