Vetted MongoDB Professionals

“Backend/data engineer with hands-on production experience across Python/Flask microservices and AWS serverless/data platforms (Lambda, DynamoDB, S3, Glue/PySpark). Demonstrated strong reliability and operations mindset (JWT/RBAC, retries/timeouts/circuit breakers, CloudWatch/SNS alerting) and measurable performance wins (SQL report runtime cut from 10 minutes to 30 seconds). Seeking ~$150k base and cannot travel for onsite meetings for the next 5–6 months due to family medical constraints.”

“Stony Brook Fall 2024 capstone contributor who built a ROS2-based warehouse mobile robot prototype, owning perception and SLAM integration end-to-end. Strong in real-time robotics optimization on Jetson Orin (TensorRT/CUDA, ROS2 tracing/Nsight) and in distributed ROS2 communications (DDS discovery/QoS, MAVLink-to-ROS2 bridging), with a full simulation/testing/deployment toolchain (Gazebo, CI tests, Docker/K3s).”

“New-grad backend engineer who built a real-time genome analysis pipeline, replacing a slow batch system with an event-driven distributed architecture in Python/Redis and a React progress dashboard. Reports ~6x improvement and cutting analysis time from days to hours with zero data loss under peak load, emphasizing reliability patterns like retries and idempotency plus API security (JWT/RBAC/HTTPS).”

“At Unar Labs, built and operationalized LLM capabilities inside a cloud-native SaaS product, emphasizing production reliability (fallbacks, observability, cost/latency/quality monitoring) and iterative improvement from user feedback. Also acts as a customer-facing technical lead—running developer demos/workshops and supporting sales through discovery, pilots/POCs, and technical walkthroughs to drive production adoption.”

“Software engineer with hands-on experience delivering production-floor applications in manufacturing environments: built a PDA-friendly web app integrated with Oracle PL/SQL and deployed it on-site in a live warehouse, then iterated via tight feedback loops. Also rebuilt a broken assembly QR label printing workflow as a WPF Windows desktop tool and rolled it out across factory processes with operator training; additionally built a TypeScript/Node/Express/MongoDB app deployed on AWS (EC2/S3).”

“Frontend engineer focused on high-scale React + TypeScript dashboards, including an internal Instagram creator/agency analytics dashboard handling extremely large datasets (1–2TB) with virtualization and performance profiling to maintain ~60fps UX. Experienced in modern state management (Redux Toolkit/RTK Query), modularizing legacy codebases into shared component libraries (Storybook), and shipping fast with feature flags plus automated QA (Playwright/Selenium).”

“AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.”

“Python backend engineer (BetterWorld Technology) who owns microservice systems end-to-end on Azure, including Kubernetes deployments, CI/CD, and production monitoring/alerting. Has hands-on experience integrating SQL/NoSQL (including Cosmos DB with vector search/graph workflow) and has built a Kafka + Spark Streaming pipeline to Snowflake with a reported 40% latency reduction.”

“Fractional CIO/founder-type operator at AHDX Inc, a hybrid-cloud/primary storage solutions startup with a compliance focus targeting healthcare; MVP has completed beta and the company has seed funding while pursuing additional investment. Has supported multiple startups (OmniHR, Combat Connect, 3BX) and cites tangible results tied to DoD/security, Medicare/Medicaid, and Air Force LMS contracts, positioning them as a hands-on "fixer" for early-stage technical strategy and architecture.”

“Software engineer experienced delivering customer-facing, real-time industrial monitoring dashboards (motors/shafts/turbines) by partnering directly with end users to refine charts, alerts, and performance. Strong in API/platform integrations and production troubleshooting—uses feature flags, logging, validation/mapping, containerization, and performance testing to keep systems stable while iterating quickly.”

“DevOps engineer (State Farm) with hands-on ownership of Python backend services and data pipelines, deploying microservices and workers on Kubernetes using GitOps (Argo CD). Has led complex cloud-to-on-prem/hybrid migrations with staged cutovers and rollback planning, and built Kafka-based real-time streaming pipelines with schema governance, autoscaling, and strong observability.”

“Backend/cloud engineer with hands-on ownership of a climate data extraction pipeline (BeautifulSoup + Pandas ETL + CRON) that automated 50k+ monthly data points and removed ~20 hours/week of manual work. Also built a multi-AZ Kubernetes deployment for a Node.js system using Terraform and GitHub Actions (blue-green, rollbacks) and has Kafka/FastAPI experience from a healthcare plan management project.”

“LLM/RAG practitioner who has taken a support-ticket triage automation system from prototype to production, building the full pipeline (fine-tuned models, FastAPI inference services, vector storage, monitoring) and delivering measurable impact (~40% reduction in triage time). Demonstrates strong operational troubleshooting of LLM/agentic workflows (observability-driven debugging, fixing agent routing/looping) and supports adoption through tailored demos and sales-aligned technical communication.”

“Built and deployed serverless AWS applications (Lambda/S3/RDS Proxy) including a NASA L’Space React + Python data analysis tool, focusing on performance for large datasets. Demonstrates strong cloud troubleshooting across compute and networking (CloudWatch-driven diagnosis, EC2 scaling, security group fixes) and a user-driven iteration loop that improved product usability with dynamic filtering and interactive UI.”

“Applied AI/ML engineer with hands-on production experience building a RAG-based AI assistant for pharmaceutical maintenance troubleshooting using LangChain + FAISS/Pinecone, including a custom normalization layer to handle inconsistent terminology and duplicate document revisions. Also built Airflow-orchestrated pipelines for document ingestion/embeddings and predictive maintenance workflows (SCADA ETL, drift-based retraining), and partnered closely with production supervisors/quality engineers via Power BI dashboards and real-time alerts.”

“LLM/RAG engineer who has built and shipped production assistants, including a RAG-based teaching assistant (Marvel AI) using LangChain/LlamaIndex/ChromaDB with OpenAI embeddings and Redis vector search, achieving ~30% accuracy gains and ~35% latency reduction. Also deployed FastAPI services on Google Cloud Run with observability and prompt-level monitoring, and partnered with non-technical ops stakeholders to deliver an internal policy-document RAG assistant.”

“Cross-functional operator/chief-of-staff style leader who took a product from prototype to a live pilot in 3 months, spanning public-sector data normalization, an ML matching engine, a secure API, and KPI/investor demo instrumentation. Strong focus on executive alignment and productivity via Notion-based operating systems plus automated reporting (Python/Power BI), with experience supporting fundraising and go-to-market narratives.”

“Backend engineer who built a Node.js + SQL service integrating with the Google Ads API to periodically upload online and offline conversions via Azure Logic Apps, persisting upload records for ROI reporting. Implemented PII hashing, token validation, redundancy, and detailed failure/status logging for reliability and debuggability. Currently scoping an LLM/agent workflow (likely LangChain) to let marketing bulk-update e-commerce product data using SEO keywords without developer involvement.”

“Full-stack engineer with payments/settlement domain experience who modernized a payment tracking workflow from REST to GraphQL and delivered a production payment status dashboard using Next.js App Router + TypeScript. Strong in performance and reliability work (Postgres indexing/Explain Analyze, Redis caching, Datadog observability) and in durable event-driven processing with Kafka (DLQs, idempotency, reconciliation, event replay).”

“GenAI/LLM engineer who built and productionized a 0-1 application (EMULaiTOR at Lumanity) combining qualitative + quantitative data using Postgres/pgvector RAG and prompt engineering, deployed with Azure backend and AWS-hosted frontend. Demonstrates strong production instincts (latency reduction via region alignment, autoscaling/health checks) and hands-on agent/tool-call debugging, plus experience enabling sales and winning a large pharma client.”

“Forward Deployment Engineer focused on application security and production integrations, with hands-on experience hardening API-driven ticketing systems (JWT/RBAC/rate limiting/log redaction) and implementing CI/CD security controls (Bandit SAST, SCA, container hardening). Strong in diagnosing peak-load production issues using logs/metrics/infra signals and driving durable fixes like adaptive throttling and backoff, while aligning engineering, business, and leadership stakeholders on risk and SLA impact.”

“Internship experience at Paycom owning an end-to-end personalized course recommendation feature for an LMS, spanning SQL-based data pipelines, ML integration, and FastAPI REST services for real-time recommendations. Focused on production tradeoffs (latency vs. accuracy), scaling/SQL optimization, and post-launch iteration driven by engagement metrics.”

“Full-stack engineer with healthcare and insurance domain experience who has owned production systems end-to-end (React/Next.js, FastAPI/Node, Postgres, AWS SNS/SQS, Docker, CI/CD) and delivered measurable impact (30% faster data processing). Also productionized an LLM-powered clinical data assistant using RAG + a vector database with guardrails and evaluation loops, cutting analyst lookup time by ~30–40%, and has experience modernizing monoliths to microservices with feature-flagged, low-regression rollouts.”

“Backend/security-focused engineer from VeroTX who built an IdP service (Spring Boot + MongoDB on GCP) for an AI workflow platform and drove major latency improvements via caching and query/index optimization. Also shipped an AI loan-processing agent using LangChain/LangGraph, owning the document ingestion + vector database layer and designing a reliable multi-step workflow with retries, monitoring, and human-in-the-loop safeguards.”