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Vetted Go Professionals
Pre-screened and vetted.
Senior Full-Stack Engineer specializing in AI/ML, LLMs, and RAG systems
Director-level Software Engineering Leader specializing in cloud platforms and large-scale systems
Staff Full-Stack Software Engineer specializing in scalable web platforms and cloud infrastructure
Mid-level Full-Stack/Backend Software Engineer specializing in distributed systems and cloud
Director-level Engineering & AI Product Leader specializing in GenAI and cloud platforms
Mid-level Full-Stack Software Engineer specializing in distributed systems and GenAI platforms
Junior Software Engineer specializing in AI infrastructure and applied machine learning
Senior Backend Engineer specializing in Healthcare & Cloud Platforms
Mid-level Data Engineer specializing in big data platforms and analytics infrastructure
Junior AI Software Engineer specializing in LLM systems and retrieval (RAG)
Senior Software Engineer specializing in AWS distributed systems and developer tools
Intern Full-Stack Software Engineer specializing in distributed systems and cloud services
Executive Engineering Leader specializing in Platform, Cloud, and AI tooling
Mid-level Software Engineer specializing in distributed systems and payments
Executive engineering leader specializing in cloud platforms, APIs, and FinTech
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.”
Kaustubh Rai
Mid-level Software Engineer specializing in distributed systems and cloud-native microservices
“Software engineer with ~2 years at UnitedHealth Group plus CMU coursework/TA experience, spanning backend modernization and cloud-native operations. Worked on migrating a customized open-source EDI system from Python 2 to Python 3 while improving SQLite database traceability via JSON exports, and has hands-on Kubernetes microservices deployment on Azure using Helm, HPA, and Jenkins-based Git-triggered CI/CD. Also built a large-scale real-time ride-hailing simulation using Kafka + Samza with explicit fault-tolerance strategies.”
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.”
Yashvi Shah
Mid-Level Software Engineer specializing in distributed systems and cloud platforms
“Amazon Alexa engineer who architected and shipped a GenAI Knowledge Agent used by 2M+ customers, focused on making LLM outputs auditable via citations and a verification layer that prevents hallucinations. Built the full vertical slice (FastAPI/LangChain backend + React/TypeScript streaming UI) while keeping p99 latency under 200ms, and has proven incident response experience on AWS (Lambda/DynamoDB scaling issues).”
Lawrence Cheng
Mid-Level Backend Software Engineer specializing in FinTech platforms
“Backend/platform-focused engineer who builds scalable onboarding and data ingestion pipelines for complex client data formats, emphasizing staged validation, idempotent job boundaries, and safe rollouts behind feature flags. Strong in production diagnostics (Kibana/Logstash, SQL, debugger traces) with a concrete example of finding a regression causing incorrect Tax Loss Harvesting alert counts within a day, and experienced enabling both engineers and customer-facing teams through docs, runbooks, and technical walkthroughs.”