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Vetted Vertex AI Professionals
Pre-screened and vetted.
Mid-level AI/ML Researcher specializing in LLMs, conversational agents, and health NLP
Mid-level AI/ML Engineer specializing in healthcare GenAI and MLOps
Mid-level Machine Learning Engineer specializing in production MLOps and healthcare analytics
Mid-level Machine Learning Engineer specializing in MLOps, GenAI RAG, and computer vision
Junior ML/Data Engineer specializing in NLP, cloud data pipelines, and real-time sentiment analysis
Mid-level AI/ML Engineer specializing in NLP, GenAI, and fraud/risk analytics
Senior Full-Stack Software Engineer specializing in AI-enabled microservices and micro-frontends
Senior Software Developer specializing in cloud-native microservices and GenAI/ML
Mid-level Machine Learning Engineer specializing in Generative AI, LLMs, and MLOps
Mid-level Machine Learning Engineer specializing in MLOps, fraud detection, and data security
Mid-level AI Engineer specializing in retail personalization and LLM-powered systems
Mid-level Machine Learning Engineer specializing in production ML, MLOps, and Generative AI
Mid-level AI/ML Engineer specializing in FinTech and production ML systems
Sridhar Duggireddy
Executive Technology Leader specializing in Cloud Platforms, DevOps, and AI/ML
“Early-stage startup CTO helping build an AI-powered parenting assistant app with features spanning advice, shopping, task management, and inventory management. The team is currently testing an MVP with their network while the candidate simultaneously learns the seed/Series fundraising process through Connectd and early investor conversations.”
sai anuragh Sangoju
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”
Yashwanth Penugonda
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production GenAI systems
“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”
Shamala Chandrappa
Mid-level AI Engineer specializing in agentic AI, LLM systems, and healthcare AI
“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”