Vetted Observability Professionals

“Meta engineer focused on reliability/operations tooling who built a unified real-time health dashboard and scalable telemetry pipelines (AWS + Datadog) for thousands of devices. Also shipped an internal LLM-powered knowledge assistant using RAG over wikis/runbooks/logs with strong guardrails and a rigorous eval loop that drove measurable accuracy improvements via automated doc ingestion and embedding updates.”

“Platform and distributed-systems leader (player-coach) who owned architecture and reliability for an Amazon analytics/data platform serving ~100K internal users at exabyte scale. Built an ML-driven “Lakeflow” optimization layer that cut pipeline completion times ~20–25% and reduced compute waste >15%, and led major incident response/redesign efforts (e.g., deletion storm) with strong rollout/observability/rollback practices.”

“ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.”

“Operations leader with Amazon experience owning a founder-level initiative during national supply chain Regionalization, building and scaling a "Stack-to-Light" operating mechanism to standardize non-sortable FC execution with instrumentation and balanced-scorecard metrics. Later joined Revivn to stabilize an underperforming operation by developing frontline leaders and aligning ops with finance and GTM through clear KPIs and operating cadence.”

“AWS engineer who productionized an internal ML-driven data pipeline from a notebook prototype into a scalable, observable Python service (schema validation, deduplication, idempotency, safe retries, versioned transforms, CloudWatch alarms), reducing manual effort and improving data accuracy/trust. Experienced diagnosing workflow issues in real time (e.g., upstream schema changes) and partnering with account managers/support to unblock adoption of seller-facing Marketplace features by demonstrating reliability with concrete metrics.”

“Backend/platform engineer with hands-on ownership of Python services (Postgres/Redis/Celery) and measurable performance gains (~20–25%). Strong Kubernetes + ArgoCD GitOps experience delivering zero-downtime rollouts, plus led key reliability fixes (readiness probes, immutable tagging) and supported an on-prem to AWS migration using CDC replication and ALB traffic shifting; also built Kafka real-time analytics pipelines with schema registry.”

“Backend engineer with healthcare platform experience at Thirty Madison, combining Django for secure, data-heavy core services with FastAPI async microservices for real-time patient monitoring. Led Kubernetes migration with Istio service mesh, autoscaling (HPA), and stateful storage patterns, and implemented GitOps CI/CD using ArgoCD. Also built real-time Kafka streaming pipelines with reliability patterns like idempotent producers and offset management.”

“Software engineer (2+ years in India) and current GenAI intern who shipped LLM-powered review-writing enhancements at Myntra (Walmart-backed), using pilots and A/B tests to lift review quality by 5% in 30 days. Demonstrates strong LLM operations discipline (logging, dashboards, alerts, rollback) and fast incident response, plus experience delivering developer-focused workshops and public technical talks.”

“Built a production RAG-based natural-language-to-SQL system at Global Atlantic to replace slow, expensive manual analytics ticket workflows, focusing heavily on retrieval quality and measurable evaluation (200-question ground-truth set; recall@5 improved 0.65→0.78 via semantic chunking). Also built a custom MCP-style agent orchestrator for a personal project (arxiv-ai) to improve flexibility and Langfuse-aligned observability, and has hands-on experience with LangGraph, CrewAI, and n8n.”

“Senior engineering leader in the Bay Area with experience spanning VMware, Hortonworks/Cloudera, Barracuda, and Palo Alto Networks, including leading open-source work (Apache Knox) and architecting large-scale security platforms. Has driven disaster recovery and cloud security products, designed Python microservices for Microsoft 365 security, and scaled teams (3x) while formalizing enterprise readiness practices with automated documentation using Notebook LLM.”

“Engineering lead/player-coach at Sam's Club who drove major React Native Member Desk Returns changes (including Vision Center insurance refund flows) by coordinating Product/UX, QA, and API teams and leading a 3-engineer squad. Also led an e-commerce mobile OAuth migration, evolving a login experience from vanilla JS to a React SPA as security requirements (MFA, reCAPTCHA) emerged, and improved production reliability via better request flagging and analytics/log instrumentation.”

“Backend/data engineer with Optum healthcare claims domain experience building high-reliability Python microservices (FastAPI/Kafka/Postgres) and AWS data platforms (EKS, Glue, Redshift). Demonstrated strong production ownership: fixed duplicate Kafka processing via transactional outbox/idempotency, scaled to millions of daily events, and delivered major SQL performance gains (40+ min to <5 min, ~60% CPU reduction). Seeking remote-only work; targets $130k base.”

“Built and shipped a production LLM-driven multi-agent platform (ciATHENA) at CustomerInsights.AI to automate analytics/ML/compliance workflows in healthcare and life sciences. Implemented LangGraph/LangChain orchestration with strong backend-style rigor (schemas, Pydantic validation, retries, auditability) and optimized latency/cost while keeping the system usable for non-technical users via guided natural-language interactions and structured/visual outputs.”

“Backend/data engineer who has owned production Python APIs and high-throughput async workflows on AWS (FastAPI, Docker, ECS/EKS/Lambda) with mature reliability practices like idempotency, bounded retries, circuit breakers, and strong observability. Also built AWS Glue ETL into an S3/Redshift lakehouse and modernized legacy batch systems via parallel-run parity testing and feature-flagged migrations, including a SQL tuning win cutting a multi-minute query to under 10 seconds.”

“Backend engineer who built an event-driven, multi-service resume review system integrating AI/ML workflows. Demonstrated strong performance engineering (e.g., composite indexing dropping latency from ~600ms to ~35ms and major P95 gains) and high-throughput pipeline optimization via caching, batching, and worker concurrency tuning, with multi-tenant isolation implemented across DB and Redis.”

“Full-stack engineer (Snowflake) who shipped an AI/LLM-powered data exploration product end-to-end, spanning Spring Boot/Python services and a polished React UI with streaming responses and robust fallbacks. Experienced operating high-scale AWS deployments (Docker/Kubernetes, SNS/SQS, RDS Postgres, CloudWatch, Jenkins CI/CD) supporting thousands to tens of thousands of concurrent users, including handling real traffic-spike scaling incidents.”