Vetted MongoDB Professionals

“Data engineer with Cigna experience building and operating an end-to-end AWS-based healthcare claims pipeline processing ~2TB/day, using Glue/Kafka/PySpark/SQL into Redshift. Strong focus on data quality and reliability (schema validation, monitoring/alerting, retries/checkpointing/backfills), reporting improved accuracy (~99%) and reduced latency, plus experience serving real-time Kafka/Spark data to downstream analytics with documented data contracts.”

“Built and shipped production RAG-based LLM agents automating multi-step document query workflows, emphasizing reliability via monitoring, retries, structured exception handling, and fallback retrieval (alternative embeddings/keyword search). Demonstrated measurable gains (18% latency improvement, 25% retrieval efficiency, 12% precision) and has experience integrating agents with messy tax and transaction data at RSM using validation/cleaning and idempotent design.”

“Full-stack engineer who built an AI-driven document analysis and processing workflow end-to-end, including large-document ingestion, queued async processing, and low-latency retrieval for user-facing flows. Demonstrated practical performance tuning (moving heavy work off request path, polling, caching) and Postgres optimization validated with EXPLAIN ANALYZE, plus durable workflow resilience via retries and dead-letter queues.”

“Backend/AI engineer who has shipped an LLM-powered enterprise support-ticket agent at Comcast, building a production-grade microservices pipeline (FastAPI, SQS, Redis) with strong observability (OpenTelemetry/Splunk/Prometheus/Grafana) and reliability patterns (async, caching, circuit breakers, idempotency). Demonstrated quantified impact at scale—processing 10k+ tickets/day while improving response SLAs and routing accuracy through evaluation and human feedback loops.”

“Backend/platform-focused engineer who has shipped production LLM agents for messy research dataset submissions, turning manual validation into an automated, reliable ingestion pipeline. Strong on production hardening (streaming large uploads, strict schema/function-calling outputs, idempotency, RBAC) plus eval/monitoring loops that improved data quality, reduced support burden, and increased adoption.”

“Built a Python-based geospatial machine learning backend for PFAS contamination risk mapping, including reproducible feature pipelines, ensemble modeling, and a FastAPI layer for visualization/analysis. Emphasizes data integrity and robustness (CRS/coverage checks, fail-fast validation) and has led safe backend refactors using feature flags, idempotent backfills, and Postgres RLS for secure, queryable results delivery.”

“Backend engineer who built a real-time, event-driven alerting platform (Java/Spring Boot, Kafka, MongoDB) processing millions of events per day on AWS (Docker/Kubernetes), including hands-on performance debugging of Kafka consumer lag at peak. Also shipped an end-to-end LLM-based alert summarization feature and designed a multi-step incident triage agent workflow with retries and human-in-the-loop escalation.”

“Full-stack engineer with production experience across React/TypeScript, Node/Express, and Java/Spring Boot, operating containerized systems on AWS (EKS/ECS/EC2/RDS/S3) with strong observability (CloudWatch/Grafana). Notable for fixing a real checkout/order-placement failure end-to-end by adding frontend submission guards and backend idempotency with Redis + Kafka deduplication, then validating impact via technical metrics and business KPIs. Has also built Kafka-based integrations/pipelines with robust retry/backfill/reconciliation patterns in retail and banking contexts.”

“Backend/real-time systems engineer transitioning into robotics software: building ROS 2 fundamentals (pub/sub, custom messages, launch files) and experimenting with Nav2 + SLAM in Gazebo/RViz. Demonstrated practical debugging by tuning costmaps/planners and analyzing topic latency to stabilize simulated navigation, and has experience integrating telemetry pipelines and REST-based external interfaces.”

“Backend-focused intern who built and refactored the backend for an LLM-driven gifting mobile app using FastAPI, tackling high-latency LLM + product-API workflows. Implemented async worker-pool/queue processing with Redis caching plus retries/fallbacks, cutting end-to-end suggestion latency from ~4–5 seconds to ~1 second while improving reliability and rollout safety via staged migrations and testing.”

“Backend/data engineer with production experience in payment initiation/processing services built in Python/FastAPI, emphasizing reliability patterns (JWT/RBAC, timeouts, retries, circuit breakers). Has delivered AWS deployments on ECS (ALB, autoscaling, CI/CD to ECR) plus Lambda-based reporting, and built AWS Glue ETL pipelines with schema evolution and CloudWatch monitoring. Also modernized a legacy SAS reporting platform to Python/PostgreSQL with regression parity testing and parallel-run migration, and achieved a 70% SQL performance improvement.”

“Backend/integration-focused engineer in the Maryland area with production experience building FastAPI REST services secured with OAuth2.1/JWT and reliability patterns (timeouts, selective retries, idempotency, centralized error handling). Has delivered AWS-integrated MuleSoft/CloudHub solutions and supported AWS Glue ETL workflows, plus demonstrated strong SQL tuning with a 30–40s to 3–5s performance improvement.”

“Accenture engineer who owned an insurance e-application end-to-end and drove incremental releases that reduced recurring production issues. Also built a TypeScript/React (Next.js) + NestJS microservices platform using PostgreSQL, Redis, Stripe, and Kafka, with strong focus on decoupling, eventual consistency, and scaling consumers under load. Created a hackathon chat-based internal assistant that used live form context and documentation-grounded answers to help agents resolve customer queries during form filling.”

“Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.”

“Full-stack engineer who built and owned an end-to-end real-time engineering dashboard for Medtronic robotic surgical systems, streaming high-frequency sensor/kinematic data via Python WebSockets to a React/TypeScript UI. Differentiates through performance/reliability practices (stable core vs experimental layer, observability, caching) and high-impact 3D visualization + session playback that became part of engineers' regular bench-testing workflows.”

“Software engineer/product owner experience at GE Healthcare, owning a patient records and claims workflow product end-to-end. Built React/TypeScript + Spring Boot systems with contract-driven APIs (OpenAPI) and operated Spring Boot microservices using RabbitMQ, focusing on reliability patterns (idempotency, DLQs) and performance improvements driven by clinical feedback. Also created an internal monitoring/deployment dashboard that became the default tool for on-call and production support.”

“Software engineer who owned an Order Management System end-to-end at Reliance Jio, improving large-table performance via UI virtualization shipped behind feature flags and refined through direct ops-user observation. Also built an OCR automation tool at Piramal Realty using Python/Tesseract with validation and manual correction fallbacks, driving adoption by operations teams. Experienced integrating with Kafka-based microservices and improving observability using structured logging and correlation IDs.”

“Built and productionized an internal LLM-powered search tool that helps engineers find the right SolidWorks macros using plain-English queries, using OpenAI embeddings and ChromaDB with strong logging/fallback safeguards. Experienced in diagnosing RAG/agentic workflow issues in real time and in hands-on API support, including fixing customer macros after SolidWorks version updates and driving adoption through reusable solutions and best practices.”

“Software engineer experienced in e-commerce systems, building customer-facing features and internal operations tools with TypeScript/React frontends and Spring Boot microservices. Demonstrated measurable performance wins (order-tracking API improved from ~2s to <700ms) and strong event-driven reliability practices with RabbitMQ (idempotency, DLQs, retry/backoff), including resolving a production queue backlog incident. Built an ops dashboard with real-time cross-service order tracing that became a daily tool for support/ops and reduced escalations to engineering.”

“BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.”

“Full-stack engineer with strong data/regulatory reporting background (BNY) who owns customer-facing and internal reporting products end-to-end—from ETL/SQL transformations through React/TypeScript UIs and Spring Boot APIs. Built role-based, audit-friendly dashboards and designed RabbitMQ-based event-driven microservices with reliability patterns (idempotent consumers, publisher confirms, Saga) to scale workflows across teams.”

“Backend/AI engineer who built an AI icon-generation SaaS backend (Java/Spring Boot, MongoDB) on AWS, including async job processing with idempotency and S3-based result storage to handle traffic spikes. Also shipped applied AI in finance—an end-to-end fraud detection pipeline with risk scoring—and designed a banking support AI agent with strict guardrails, audit logs, and human-in-the-loop escalation.”

“Software engineer with experience spanning high-scale backend systems and distributed consensus: led a 6-person team delivering a production data querying/visualization platform with major latency improvements via cursor-based pagination and streamed results. Built a RAFT-based distributed logging tool resilient to partitions and storage constraints, and at Nasuni developed FastAPI services processing multi-terabyte workloads for 500+ enterprise customers with secure API key management.”

“Backend engineer currently building an AWS Lambda/FastAPI inventory recommendation system using a LangChain + GPT-4 RAG pipeline and MongoDB vector search; drove major cost optimization via Redis caching (60% reduction) while sustaining 10k+ daily requests under 2s latency. Previously deployed Node.js microservices on AWS OpenShift with Jenkins/Helm at UnitedHealth Group and led a zero-downtime monolith-to-microservices migration at Verizon, including RabbitMQ-based real-time messaging with DLQs and idempotency.”