Vetted Observability Professionals

“Backend/Node.js-focused engineer who improved a widely used shared config/logging utility library by fixing a real-world async race condition (single disk read under concurrency) and adding stronger validation/testing, resulting in more deterministic services and faster startup/build/CI times. Also builds internal platform automation spanning Python/Go/TypeScript with strong documentation practices and security-conscious customer onboarding (e.g., sensitive Kubernetes clusters, HashiCorp Vault access issues).”

“Full-stack TypeScript engineer who has owned a real-time workflow/communication platform end-to-end in production (Node/TS + React, Postgres/Redis, Kafka, Docker/CI/CD). Demonstrates strong distributed-systems pragmatism—designing for failure with retries, DLQs, idempotency keys, and atomic writes—plus operational practices like structured logging, monitoring, and zero-downtime deployments.”

“CTO-level leader who drove a major post-buyout transformation at NPact—modernizing engineering (CI/CD, QA, observability), moving products toward SaaS/cloud, and scaling the org from ~20 to ~70 while maintaining 97% retention. Uses instrumentation and workflow analytics (including Atlassian-derived data) to improve delivery, citing an ~80% reduction in feature/bug churn through better scoping and requirements. Comfortable with board-level ROI decisions and customer/fundraising conversations, translating technical tradeoffs into clear business outcomes.”

Omar Rahmoun

Screened

Senior Software Engineer specializing in distributed systems and AI platforms

Chicago, IL5y exp

“Senior engineer transitioning into a lead engineer role who is actively overseeing 5 developers and championing an AI-first development culture. Stands out for a highly structured approach to AI-assisted software delivery, including context engineering, phased planning, multi-agent orchestration, and deliberate hallucination mitigation rather than 'vibe coding.'”

“Repeat founder/CTO with hands-on experience raising capital from friends and family, angels, corporate sources, federal grants, private equity, and venture capital. Built a startup in a software business incubator, later sold the company, and went on to serve as an Engineering Manager at the acquirer inside the Plug and Play accelerator ecosystem.”

“Backend/data platform engineer who owned an enterprise Django REST + PostgreSQL reporting backend and built Python ETL pipelines to normalize 3M+ legacy customer records, improving data reliability by 85%. Strong Kubernetes/GitOps practitioner (Helm, ArgoCD, Jenkins/GitHub Actions) with real-world production debugging experience, plus Kafka streaming at 5M events/day and a zero-downtime monolith-to-event-driven microservices migration on AWS that cut infra costs by 42%.”

“Full-stack engineer who built a live-streaming edtech platform at KratosIQ, owning the entire frontend and the backend streaming layer. Notably migrated the system from a P2P mesh to an SFU architecture to handle scaling under heavy load, and delivered measurable React performance gains (450ms to 40ms render time) validated via Lighthouse and web vitals.”

“Robotics software engineer focused on backend/integration for indoor autonomous mobile robots, with hands-on ROS 2 experience integrating Nav2/AMCL/TF2 and LiDAR/camera pipelines. Emphasizes production readiness—robust failure recovery, QoS-tuned distributed communication, and strong observability (logging/health checks)—validated through Gazebo simulation, sensor-data replay debugging, and Docker-based CI/CD deployment.”

“LLM/agentic systems builder who has deployed production solutions for a resource management firm, using an MCP-driven architecture with Neo4j + Elasticsearch and a ChatGPT frontend to generate candidate/company “SmartPacks” and answer entity Q&A. Also built a LangGraph/LangSmith-orchestrated multi-agent workflow that automates data-infra change requests end-to-end (impact analysis, SQL + tests, and PR creation), and delivered a ~60% latency reduction through TTL-based context caching while improving accuracy via a business data dictionary.”

“Built and deployed a production, multi-tenant modular agentic AI platform at Easybee AI, using LangChain/LangGraph with Redis-backed durable state to make agents reusable, traceable, and auditable. Emphasizes reliability via strict tool schemas, deterministic controllers, tenant-level policy enforcement, and regression testing derived from real production failures; also delivered AI automation for legal/finance workflows (attorney draw and expense automation) with explainable, deterministic payouts.”

“Backend/AI engineer who has built and operated production Node.js/Express services on AWS (Postgres/Redis) and has hands-on experience shipping an AI-powered support agent using RAG (Pinecone + LLM) with grounding checks and evaluation for hallucination rate. Demonstrates strong production reliability/performance debugging, including reducing peak latency from ~2s back to sub-300ms through query and caching optimizations, plus designing agent workflows with retries and human-in-the-loop escalation.”