Vetted Workflow Automation Professionals

“Revenue/sales operations and GTM builder for early-stage startups who designs repeatable sales processes (CRM pipeline stages, MEDDPICC, Challenger Sales) and operating cadences (OKRs) to improve conversion and forecasting. Also mentors product/technical founders—e.g., through the Canadian Technology Accelerator—helping a fintech founder establish segmentation, metrics, and an outbound motion that produced early recurring revenue and investor readiness.”

“Backend/LLM engineer who ships production internal tooling end-to-end: automated data-request processing with monitoring-driven improvements (better error diagnostics and lower latency via query/index tuning). Also built a RAG-based internal Q&A system over company docs and operational logs with guardrails (similarity thresholds, fallbacks, response limits) and an eval loop using real user queries and human review to drive prompt/retrieval changes.”

“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%.”

“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 engineer (IpserLab) who owned Python services for a production quiz/analytics platform, focusing on reliability and low-latency behavior under peak load. Hands-on with Kubernetes + Docker deployments and GitHub Actions CI/CD in a GitOps-style workflow, including solving configuration drift and enabling fast rollbacks. Also implemented Kafka-based event streaming with idempotent consumers and strong observability (lag tracking, structured logging, alerting).”

“B2B lifecycle/CRM marketer running highly technical scientific service-line campaigns (multi-omics, translational sciences, ddPCR) using HubSpot segmentation, lead scoring, and multi-channel execution (paid social, ABM, and content syndication). Delivered measurable pipeline impact (200+ leads, 120 MQLs) and improved conversions 5–6% through cross-functional ICP refinement and multivariate testing.”

“CTO/Chief Architect who rebuilt an IoT platform from a fragile legacy stack into an AWS-based, multi-tenant cloud-native system supporting 50k+ connected devices and 10M+ monthly events, then layered in real-time data pipelines and ML anomaly detection. Known for tightly aligning roadmaps and OKRs to business KPIs (onboarding speed, uptime, velocity) and for scaling teams into domain-focused pods; previously led a shift from LAMP to event-driven Node.js microservices using MQTT and message queues.”

“Unity developer from the Naer project who built a natural-feeling VR wrist-flick gesture mechanic for teleport/push/pull interactions, validated through telemetry, low-FPS simulation, and diverse user testing with modular ScriptableObject tuning. Also handled Photon PUN 2 networking bug fixes (mid-air item state issues) and RPC optimizations while integrating AI tools (Claude/Gemini/ChatGPT) into planning and implementation.”