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Vetted MongoDB Professionals
Pre-screened and vetted.
Mid-level Full-Stack/Backend Software Engineer specializing in distributed systems and cloud
Senior Technology Consultant specializing in cloud, data engineering, and AI solutions
Mid-level Generative AI & Machine Learning Engineer specializing in LLMs and RAG
Mid-level Data Engineer specializing in cloud-native big data pipelines and analytics
Senior Backend Engineer specializing in Healthcare & Cloud Platforms
Mid-Level Full-Stack Software Developer specializing in AWS cloud and automation
Senior Data Engineer specializing in cloud data platforms and real-time analytics
Junior Frontend Software Engineer specializing in React, TypeScript, and performance optimization
Mid-Level Full-Stack Software Engineer specializing in cloud-native systems and FinTech
Senior Software Engineer specializing in AWS distributed systems and developer tools
Mid-level Software Engineer specializing in backend systems, billing, and real-time data pipelines
Principal Data Scientist / AI Engineer specializing in healthcare-native AI platforms
Director-level Engineering Leader specializing in SaaS platforms, cloud architecture, and SRE
Rhutwij Tulankar
Engineering Manager and ML/Data Architect specializing in scalable data platforms and personalization
“Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.”
Vigynesh Bhatt
Mid-level Software Engineer specializing in backend, cloud, and ML systems
“Software engineer with experience across Goldman Sachs, BYU Broadcasting, Juniper Networks, and an edtech startup (Doubtnut), spanning data migrations, AWS-based media backends, and microservices observability. Built a Redis/ElastiCache caching layer in front of DynamoDB/S3 to improve media delivery latency and cost, and created an SEO indexing automation tool using the Google Search Console API that saved ~15–30 person-hours per day.”
Alexander Choy
Director of AI/ML Engineering specializing in MLOps, data platforms, and 3D computer vision
“Backend/data engineer focused on production ML/LLM systems: built a real-time FastAPI inference API on Kubernetes with strong reliability patterns (timeouts, idempotent retries, centralized error handling). Delivered AWS platforms using EKS + Lambda with GitHub Actions/Helm CI/CD and built Glue-based ETL from S3/Kafka into Snowflake with schema evolution and data-quality controls; also modernized legacy analytics/recommendation workflows into Python services with safe, feature-flagged cutovers.”