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Vetted JavaScript Professionals
Pre-screened and vetted.
Senior Backend Engineer specializing in Healthcare & Cloud Platforms
Junior Robotics Engineer specializing in autonomous systems and robot learning
Mid-Level Product Designer specializing in UX, visual, and motion design
Mid-Level Full-Stack Software Developer specializing in AWS cloud and automation
Junior Frontend Software Engineer specializing in React, TypeScript, and performance optimization
Mid-Level Full-Stack Software Engineer specializing in cloud-native systems and FinTech
Junior AI Software Engineer specializing in LLM systems and retrieval (RAG)
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
Intern Full-Stack Software Engineer specializing in distributed systems and cloud services
Mid-level AI/ML Engineer specializing in LLMs, ranking systems, and MLOps
Executive Engineering Leader specializing in Platform, Cloud, and AI tooling
Principal Data Scientist / AI Engineer specializing in healthcare-native AI platforms
Director-level Engineering Leader specializing in SaaS platforms, cloud architecture, and SRE
Junior Software Development Engineer specializing in AWS automation and data pipelines
Mariana Marini
Director-level HR & Talent Acquisition leader specializing in aviation and employee relations
“15-year Talent Acquisition leader in the airline industry who has run large, high-volume recruiting operations (6,000–8,000 hires/year) across airport ops, maintenance, IT, and inflight roles. Known for rebuilding TA workflows and ATS operations (iCIMS) to automate communications and improve compliance transparency—cutting onboarding from 6 to 2 weeks and improving time-to-fill—while also delivering multi-source Power BI KPI dashboards and leading change management for major ATS transitions (Taleo to SAP SuccessFactors).”
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.”
Ashish Ernest Jeldi
Senior Data Scientist specializing in LLMs, agentic AI, and MLOps
“Built and shipped a production agentic LLM tool that helps internal teams update technical product whitepapers using plain-language edit requests, with strong guardrails (citations, verification, refusal/clarify flows) to reduce hallucinations and maintain compliance. Experienced taking LLM workflows from rapid LangChain prototypes to more predictable, debuggable LangGraph agent graphs, and orchestrating end-to-end ingestion/embedding/indexing/eval/deploy pipelines with Kubeflow.”
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.”