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Vetted Workflow Automation Professionals
Pre-screened and vetted.
Michael Erfman
Senior Software Engineer specializing in enterprise platforms and data engineering
“Backend/data platform engineer who owned an enterprise Django REST + PostgreSQL reporting backend and built Python ETL pipelines to normalize 3M+ legacy customer records, improving data reliability by 85%. Strong Kubernetes/GitOps practitioner (Helm, ArgoCD, Jenkins/GitHub Actions) with real-world production debugging experience, plus Kafka streaming at 5M events/day and a zero-downtime monolith-to-event-driven microservices migration on AWS that cut infra costs by 42%.”
Atharva Joshi
Mid-level GenAI Engineer specializing in RAG systems and AI agents
“LLM/agentic systems builder who has deployed production solutions for a resource management firm, using an MCP-driven architecture with Neo4j + Elasticsearch and a ChatGPT frontend to generate candidate/company “SmartPacks” and answer entity Q&A. Also built a LangGraph/LangSmith-orchestrated multi-agent workflow that automates data-infra change requests end-to-end (impact analysis, SQL + tests, and PR creation), and delivered a ~60% latency reduction through TTL-based context caching while improving accuracy via a business data dictionary.”
Othmane Benyoucef
Executive AI & Data Engineering Leader specializing in LLM, RAG, and agentic AI systems
“Founder of a consultancy for 14 years with extensive experience managing multiple clients and projects simultaneously. Interested in leveraging AI to solve operational challenges and is seeking exposure to the venture capital/studio/accelerator ecosystem despite no direct VC experience.”
Atharva Pandkar
Junior AI/ML Engineer specializing in LLM agents and RAG systems
“Built and deployed a production, multi-tenant modular agentic AI platform at Easybee AI, using LangChain/LangGraph with Redis-backed durable state to make agents reusable, traceable, and auditable. Emphasizes reliability via strict tool schemas, deterministic controllers, tenant-level policy enforcement, and regression testing derived from real production failures; also delivered AI automation for legal/finance workflows (attorney draw and expense automation) with explainable, deterministic payouts.”
Mid-Level Full-Stack Product Engineer specializing in Next.js, React, and Postgres
Senior SharePoint Developer specializing in Power Platform and enterprise Microsoft 365 solutions
Director-level Product Leader specializing in enterprise & internal platforms
Mid-Level Full-Stack Developer specializing in automation and AI pipelines
Mid-level AI/ML Data Engineer specializing in secure ML pipelines and AI governance
Principal Power Platform Architect specializing in Microsoft 365 and federal enterprise solutions
Mid-Level Full-Stack Software Developer specializing in AWS cloud and microservices
Mid-level Data Engineer specializing in FinTech and AI-ready data platforms
Senior Strategy & Operations Leader specializing in SaaS and FinTech revenue enablement
Director-level Talent Acquisition leader specializing in AI-native hypergrowth SaaS scaling
Senior Full-Stack Software Engineer specializing in AI-native enterprise products
Senior Product Marketing & Research Strategist specializing in AI-enabled GTM for SaaS and Real Estate
Senior Performance Marketing & Marketing Operations Manager specializing in automation and analytics
Senior Lifecycle Marketing Manager specializing in email, SMS, and push automation
Mid-level Full-Stack Software Developer specializing in cloud microservices and healthcare interoperability
Director of Growth Operations specializing in DoD capture, cybersecurity, and C5ISR
Executive Engineering Leader & Software Architect specializing in FinTech and AI-first platforms
Director-level sales leader specializing in SMB financing and alternative lending
Priyanka Ramesh
Junior Full-Stack Software Engineer specializing in Java/Spring Boot and React
“Backend engineer (IpserLab) who owned Python services for a production quiz/analytics platform, focusing on reliability and low-latency behavior under peak load. Hands-on with Kubernetes + Docker deployments and GitHub Actions CI/CD in a GitOps-style workflow, including solving configuration drift and enabling fast rollbacks. Also implemented Kafka-based event streaming with idempotent consumers and strong observability (lag tracking, structured logging, alerting).”