Vetted Observability Professionals

“Built and deployed a production RAG-based AI assistant for sales reps to unify access to product info, pricing, and internal documents across multiple systems. Implemented ETL pipelines for normalization/chunking/embeddings, integrated the assistant into internal React/TypeScript UIs with user-specific context, and enforced security with private vector storage and permission-filtered retrieval.”

“Front-end engineer with experience at Optum and Wells Fargo maintaining internal React/Angular component libraries and design-system-aligned UI modules used across multiple apps. Known for stabilizing shared libraries via semantic versioning, Jest test automation, and high-quality documentation, plus measurable performance wins (≈40% faster dashboard loads) through profiling-driven React and API optimizations.”

“Full-stack Java developer (~4 years) who built a telecom asset management system end-to-end with React and Spring Boot, and led/participated heavily in migrating it from a monolith to Spring Cloud-based microservices. Experienced with high-volume, data-driven workloads using Kafka (partitioning, batching, resilient consumers) and production observability via centralized logging with ELK and Splunk.”

“JavaScript full-stack engineer with experience at EY building secure, cloud-ready React/Node.js applications on AWS and currently at startup Juego Juegos owning the AWS backend and CI/CD via AWS Amplify. Demonstrated impact through performance tuning of a React analytics dashboard (reduced initial load time ~20%) and resolving real payment failures by debugging Stripe 3DS flows and updating AWS Lambda plus frontend error handling.”

“Backend/platform engineer focused on real-time financial fraud detection and transaction monitoring, building low-latency FastAPI + Kafka systems with strong reliability patterns (DLQs, idempotency) and cloud observability. Has hands-on Kubernetes delivery across AWS EKS and Azure AKS with automated CI/CD and GitOps-style deployments, plus experience migrating legacy C# / Java monoliths to containerized microservices using Terraform/ARM and zero-downtime rollout strategies.”

“Backend engineer who built a stateless Python/Flask service supporting a healthcare-document ETL pipeline, offloading heavy processing to Celery workers and adding strong observability (metrics, structured logs, audits). Demonstrates practical performance/reliability work: batch chunking, priority queues, autoscaling by queue depth/CPU, DLQ routing, and PostgreSQL tuning (indexes, pagination) to cut slow API responses. Also has experience deploying real-time ML classification via TensorFlow Serving behind a FastAPI wrapper and integrating models via REST/gRPC.”

“Engineering/technology leader with experience at Evocative and through a merger with Hivelocity, aligning tech roadmaps to managed services growth. Led multi-region self-hosted cloud and automation initiatives that cut delivery time from days to hours/minutes and informed cost-saving infrastructure decisions (reported $500K OPEX savings). Known for scaling teams with pod ownership, agile/intake governance, and disciplined rollout practices that protect uptime and security.”

“AI engineer with production experience building a LangGraph-based, stateful multi-agent system at MetLife to automate complex insurance claims adjudication, integrating document discovery, Azure Document Intelligence OCR/extraction, and health data analysis. Strong in agent orchestration and production deployment (Docker + FastAPI REST APIs), with a structured approach to reliability, evaluation, and stakeholder-driven requirements.”

“Hands-on engineer focused on taking complex/LLM-style workflows from prototype to production, emphasizing robust error handling, CI/CD + Docker deployment, and deep observability (Kibana/Rancher/Grafana). Experienced customizing client integrations and data transformations (XML/JSON/PDF to SFTP) and debugging agent workflows with traces, prompt verification, and human-in-the-loop safety controls. Partnered with product/ops to drive adoption, including a MuleSoft migration that improved partner onboarding speed by ~50%.”

“IBM Power/AIX infrastructure engineer with hands-on production experience across Power8/Power9 frames, VIOS and HMC, including resolving a production LPAR outage caused by vFC mapping issues. Has operated PowerHA clusters for critical finance workloads, running quarterly failover tests and handling an unplanned failover triggered by a network adapter failure, then improving resilience with redundancy and monitoring automation.”

“Product/full-stack engineer with analytics-dashboard experience at Kantar, owning features end-to-end from React/Next.js UI through Postgres data modeling and query optimization. Built a multidimensional filters/tags module that cut analyst discovery time by ~60% and also implemented durable backend workflows for bulk report generation with retries and idempotency, validated via EXPLAIN ANALYZE and production monitoring.”

“Built and launched a verified listings system for Burrow (student subleasing) after interviewing ~50 students about scam/fake listing concerns; chose a lightweight .edu-based verification approach to ship fast and then iterated with badges and clearer details, reducing churn from 15% to 7%. Also ran an LLM A/B test for auto-generating listing descriptions and improved trust/accuracy by updating prompts to prevent hallucinated details.”

“Software engineer who delivered a dynamic service-fee system for a Belgian online grocery e-commerce platform by carving out a Spring Boot microservice from a monolith, integrating Google distance APIs, Redis caching, and CI/CD for production rollout. Also built an OpenAI-powered university chatbot with agent/workflow orchestration during academia, emphasizing availability and fallback behavior.”

“Full-stack engineer with banking-domain experience (Cognizant/Kotak) building and optimizing high-usage transaction/account APIs on Spring Boot/Node/PostgreSQL in AWS/Docker, including peak-load performance fixes. Also built an end-to-end retail demand-forecasting feature during a master’s program, spanning data pipelines, ensemble models, dashboards, and operational guardrails like validation and fallbacks.”

“DevOps/Infrastructure engineer with hands-on production experience building Jenkins CI/CD pipelines that provision Kubernetes infrastructure and process data into a MapR cluster. Uses Terraform to provision AWS resources (EC2, S3, VPC, subnets) with remote state in S3, separate environment state files, and code review/validation practices; targeting $135k base.”

“Founding/early engineer experience across Asante and a Series A startup (Adgency), shifting from data science/ML into owning production full-stack systems end-to-end. Built core product flows (registration, business profiles, map service), AWS-deployed gRPC microservices with CI/CD, and operated low-latency agent/video ad generation workflows with retries/fallbacks and PostHog-based observability.”

“Backend/AI engineer with production experience in payments/reporting systems and high-scale Node/NestJS services on AWS (ECS/ALB) using PostgreSQL, Redis, Kafka, Prisma, and Datadog. Shipped applied AI features including a Zendesk-embedded support copilot (summarization, draft replies, internal doc retrieval, playbook next steps) and an LLM-driven ops workflow agent with robust error taxonomy, retries/escalation rules, and auditability.”

“Software engineer with JPMorgan Chase experience delivering end-to-end fintech features (Next.js/React/Node/Postgres on AWS) and measurable performance gains. Built and productionized an AI-native credit decisioning workflow combining LLMs, vector retrieval, and a rules engine with strong governance (bias checks, auditability, human-in-loop), improving precision and cutting underwriting turnaround time by 40%.”

“Backend/ML engineer who built a job-search automation SaaS using a modular Selenium ETL pipeline, rigorous testing/observability, and a cost-optimized two-pass LLM ranking approach. Has led high-integrity data extraction from messy multi-city PDF records (95% integrity) and managed modular production rollouts for a 20+ engineer team, with a strong security focus (deny-by-default, row-level access control) in an AI-assisted grading platform.”

“Backend/data engineer focused on high-scale, event-driven AWS ingestion systems (SQS/Lambda/EKS) processing millions of events per day, with strong reliability patterns (idempotency, DLQs, bounded retries) and deep observability using Datadog distributed tracing. Has delivered Terraform/GitHub Actions CI/CD and improved secret rotation via Secrets Manager + IRSA, plus Glue-based ETL with schema-evolution handling and Postgres SQL optimization (including JSONB/GIN indexing). Candidate is currently living outside the US and states they do not have US work authorization.”

“Software engineer who has shipped both core web platform features (secure user authentication/profile management) and production LLM systems. Built an internal documentation knowledge assistant using a full RAG pipeline (OpenAI embeddings, vector DB, semantic search, reranking) with evaluation loops and a scalable document-ingestion pipeline for PDFs/FAQs, iterating based on metrics and user feedback.”

“Customer-facing engineer who led recurring midnight ERP data-feed/B2B integrations from prototype to production, building reusable APIs and using Hangfire for job scheduling. Known for tight weekly customer iteration, strong documentation and test coverage (80%+), and cross-functional problem-solving with Operations/Quality/NPI to resolve data-collection and manufacturing-process constraints; has 2 customers live on the integration.”

“Built a production AI fraud/risk scoring platform at BlueArc that ingests web business/product/site data, generates text+image embeddings, and connects entities in a graph to detect reuse patterns and links to known bad actors. Optimized for scale with incremental graph re-scoring and delivered investigator-friendly explainability by surfacing the exact signals/relationships behind each score; orchestrated workflows with Airflow and GCP event-driven components (Pub/Sub, Dataflow, Cloud Run) and has recent LLM workflow orchestration experience (retrieval, prompting, scoring).”