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Vetted Vertex AI Professionals
Pre-screened and vetted.
Mid-level AI & Machine Learning Engineer specializing in computer vision and MLOps
Senior Software Engineer specializing in healthcare integrations, microservices, and AI-assisted systems
Mid-level Full-Stack Software Engineer specializing in cloud-native web and real-time systems
Mid-level AI Engineer specializing in LLMs, RAG chatbots, and cloud AI testing
Mid-level AI/ML Engineer specializing in NLP, Computer Vision, and Generative AI
Staff Machine Learning Engineer specializing in Generative AI, MLOps, and Computer Vision
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices
Principal AI Platform Architect specializing in agentic AI and enterprise LLM infrastructure
Principal Machine Learning Architect specializing in AI platforms and data science
Senior Data Engineer specializing in cloud lakehouse platforms and healthcare data
Principal Full-Stack Engineer specializing in AI platforms and enterprise systems
Mid-level Generative AI Engineer specializing in LLMs, NLP, and multimodal systems
Executive Founder/CEO specializing in AI platforms for healthcare and enterprise automation
Director-level Engineering Leader specializing in SaaS, cloud, and AI/ML
Principal software engineer and technical founder specializing in AI platforms
Ashutosh Choudhari
Mid-level AI/ML Engineer specializing in LLM applications and cloud-native systems
“LLM engineer who has shipped production AI systems, including an RFP requirements extraction platform (OpenAI o4-mini + Azure AI Search + FastAPI) achieving 90%+ accuracy and ~5x throughput through grounding, structured outputs, parallelization, and caching. Also partnered with legal/compliance stakeholders at Nexteer Automotive to deliver an AI document comparison tool with traceability and confidence indicators, adopted by non-technical users and saving ~2 FTEs of review time.”
Devisri Veeramachaneni
Senior Software Engineer specializing in cloud backend systems and LLM-powered agents
“Amazon Fire TV Devices engineer who built and shipped a production LLM-powered lab triage and validation system that grounds recommendations in internal runbooks/known-issue data and pushes evidence-based actions via dashboards and Slack. Emphasizes safety and measurability with structured JSON outputs, replay-based evaluation on historical incidents, and production metrics (e.g., disagreement rate and time-to-first-action), plus cost/latency optimizations like caching, batching, and rule-based fast paths.”