Vetted MongoDB Professionals

“CloudData engineer who productionized an LLM assistant for a warehouse/logistics customer by wrapping it as a versioned, containerized API with guardrails, deterministic post-processing, and full observability. Experienced diagnosing real-time RAG/agentic incidents (latency spikes and confident-wrong answers) using trace-based isolation, replay in staging, retrieval tuning, and canary releases. Regularly runs technical demos/workshops and partners with sales on security/IAM, SLAs, and pilot rollouts to drive adoption.”

“Co-founder at Skarbe who built “Oskar,” an autonomous sales agent that handles inbound email, lead qualification, and automated follow-ups using a multi-agent architecture (OpenAI Agents SDK) with a human-in-the-loop learning phase for reliability. Also owned scaling and reliability of Gmail/Outlook sync, including a Node.js-to-Rails migration and incident response during a Product Hunt launch that generated hundreds of signups and a ~2M-email queue.”

“QA automation engineer with hands-on ownership of both UI and API test automation, including building an API test module from scratch to validate all Swagger-exposed endpoints with positive/negative coverage and pairwise optimization. Experienced stabilizing flaky Cypress tests through improved selectors (data-cy) and API-call synchronization, and integrating tiered test suites into GitLab CI with merge/release gates plus JUnit/HTML reporting and Slack notifications.”

“Built a contract and customer documentation retrieval solution for Urban Studio, designing a RAG + Elasticsearch hybrid search stack (RRF + cross-encoder reranking) with a strong emphasis on chunking/data quality and hallucination reduction. Experienced in diagnosing LLM workflow issues via observability traces and tailoring technical demos to developer concerns like reliability and high concurrency.”

“Built and shipped a production AI booking/quoting system for a Spanish-speaking cleaning business serving English-speaking customers, covering the full booking and payment flow and generating bilingual SEO/AEO content. Uses Gemini/Genkit with multi-agent orchestration (ADK/MCP, LangChain) and a production stack on Vertex AI + Cloud Run + Terraform, with analytics wired from Google Analytics to BigQuery for measurable agent performance.”

“Full-stack engineer with experience at Western Union and Aptly (for Microsoft), building production systems spanning React/TypeScript frontends and .NET Core/microservices backends. Has delivered an engineer-facing diagnostics/configuration console with TanStack Query caching/background refresh and has hands-on experience hardening transaction-processing workflows with Kafka, Azure Functions, and Resilience4j, plus Postgres modeling and query optimization.”

“Full-stack/AI engineer who has shipped production systems spanning real-time analytics dashboards and an internal LLM-powered knowledge assistant. Experienced with RAG pipelines (embeddings/vector DB, semantic retrieval, query rewriting) plus evaluation loops and guardrails, and builds observable Kafka-based data pipelines monitored with Prometheus/Grafana.”

“Backend engineer who evolved Coca-Cola bottlers' Trade Promotion Optimization platform at Coke One North America, building domain-focused microservices in Node.js and Python (Flask/FastAPI) with PostgreSQL. Experienced in multi-tenant security (OAuth2/JWT, RBAC, row-level scoping by bottler/region), API contract/versioning discipline, and Azure DevOps-driven incremental rollouts with strong observability.”

“Full-stack/ML-leaning engineer who has shipped production-grade real-time analytics and an internal AI support assistant using RAG over enterprise documentation. Demonstrates strong systems thinking across scalability, reliability, observability, and LLM safety/evaluation (thresholded retrieval, RBAC, response validation, regression-gated evals), with concrete iteration based on performance metrics and user feedback.”

“Built multiple systems from scratch at DSSD and Aglint, including an NGO sustainability reporting dashboard and a production LLM-powered phone screening agent using Twilio/Retell AI with RAG grounded in PostgreSQL candidate/job data. Strong focus on real-world reliability: guardrails, monitoring, and lightweight eval/regression loops that reduced recruiter score overrides by ~30%. Currently on OPT through May 2026 (plans STEM OPT extension) and committed to relocating to NYC for in-person work; seeking $90k–$120k base with meaningful equity for founding engineer roles.”

“Data engineer who has owned production pipelines end-to-end—from Kafka/Airflow ingestion through SQL/Python validation and dbt transformations into Redshift/BI. Also built and operated a large-scale distributed web scraping platform (50–100 sites daily, ~5–10M records/day) with Kubernetes, Kafka queues, robust retries/DLQ, anti-bot measures, and backfill-safe raw HTML storage.”

“B2B product builder with prior experience taking products from 0-1 and scaling to revenue; previously implemented e-commerce search and turned it into a monetized paid-results/bidding platform requiring architectural changes and GTM alignment. Now exploring a startup idea focused on retention and upsell for B2B companies, targeting the underserved long-tail partner segment and already validating the gap with industry leaders and POC conversations.”

“Backend engineer experienced in evolving high-scale legacy on-prem systems into cloud-native, event-driven microservices on AWS/Kubernetes (noted peak traffic ~1.5M QPS). Strong focus on reliability engineering and operational excellence—SLO-driven observability, GitOps/canary rollouts, chaos testing, and preventing cascading failures (e.g., retry-storm mitigation).”

“Backend/ML engineer focused on clinical AI transparency who built ShifaMind, an explainability-enforced clinical ML system using UMLS/MIMIC-IV/PubMed data with RAG, GraphSAGE, and cross-attention. Demonstrated strong production engineering via FastAPI API design and safe migrations (feature flags/shadow inference), plus HIPAA-aligned auth/RLS patterns; also delivered a real-time comet detection system reaching 97.7% accuracy.”

“Built Meet.AI end-to-end and made concrete architecture/performance decisions (RPC with type-safe integration; SSR + query prefetching for instant data display). Also created a Python tool at Abbott to resynchronize Ansible inventories and eliminate manual intervention by scheduling it in a Jenkins pipeline; has hands-on Docker/microservices experience including serving a pretrained LLM.”

“Backend engineer with legal-tech and AI workflow experience: built JurisAI, an end-to-end legal research system using OCR + embeddings + Pinecone vector search to deliver citation-grounded LLM answers with safe failure modes (~90% recall@K). Also led a GW Law metadata migration into Caspio with batch validation and parallel rollout, and has strong FastAPI/GCP production reliability and observability practices.”

“Backend/data engineer with hands-on production experience across Azure and AWS: built FastAPI + PostgreSQL services with Azure AD OAuth2/JWT auth and strong reliability patterns (timeouts, retries, correlation IDs). Delivered AWS Lambda/ECS solutions with Terraform/CI-CD and cost controls (SQS buffering, reserved concurrency), and built/operated AWS Glue ETL pipelines into Redshift while modernizing legacy SAS reporting into Python microservices with parity testing.”

“Robotics software engineer who built the perception-to-planning pipeline for an autonomous bipedal robot: synchronized and fused 3D LiDAR + multiple depth cameras in ROS1 (tf2/message_filters) to produce combined point clouds and 2.5D maps, then implemented and benchmarked A*/D*/RRT plus hybrid A* footstep planning in simulation. Also founded a software company and personally owned CI/CD, security checks (CodeQL), and release automation (custom release bot + Slack notifications).”

“Backend/data engineer who has built production AI video generation services on AWS using a hybrid serverless + container architecture (FastAPI, Lambda, ECS, Postgres/Redis) with strong reliability practices (auth, retries/timeouts, structured logging, CloudWatch + Slack alerting). Also delivered AWS Glue ETL pipelines with schema evolution handling and modernized a legacy SAS healthcare reporting workflow to Python with parity validation and parallel-run migration.”

“Full-stack engineer who built an AI-based inventory/procurement query system at Botlily/Botlerly using Flask and Google Sheets as a live knowledge base, overcoming Sheets latency with caching and structured in-memory models. Demonstrated strong LLM product engineering (40% accuracy improvement via preprocessing/prompting) and customer-driven iteration with bar/restaurant owners, evolving the tool into a more comprehensive inventory management and forecasting solution.”

“Built and deployed “ApartmentFinder AI,” a multi-agent system using Google ADK, Gemini, and Google Maps MCP to automate apartment shortlisting and commute-time analysis, cutting a 45–70 minute user workflow down to ~30 seconds. Also has strong delivery/process chops from serving as an SDLC Release Coordinator, managing 52+ releases and reducing SDLC issues by 84%.”

“Led an end-to-end build of bhangrascape.ca, making key architecture choices across Next.js/Node/Postgres on AWS and implementing S3-based media storage with presigned URLs. Strong focus on quality and reliability via Zod validation, Jest tests, Postman endpoint testing, and CI/CD with GitHub Actions, plus hands-on UX ownership from Figma prototyping through reusable React components and A/B testing.”

“Built and deployed a production GenAI resume screening and matching system for Florida Atlantic University, focused on improving recruiter efficiency and search relevance. Demonstrates strong RAG engineering (embeddings, query rewriting, metadata filtering, threshold tuning) plus practical reliability work (grounding constraints, fallbacks, and evaluation using real user queries) using Python REST APIs and orchestration frameworks like LangChain and LlamaIndex.”

“Built a production Apple-focused LLM Q&A bot that answers user issues using similar past discussion records, including large-scale scraping and cleaning of thousands of forum threads. Used BeautifulSoup + Playwright for static/dynamic extraction, PySpark + NLP for preprocessing, and LangChain RAG with a custom response-likeliness metric to evaluate performance.”