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Vetted AI & Machine Learning Professionals in the Bay Area
Pre-screened and vetted in the Bay Area.
Popular Titles
Junior Full-Stack & ML Engineer specializing in analytics and applied NLP
Senior AI Sales & Client Success Leader specializing in Generative AI and Gaming
Mid-level Machine Learning Engineer specializing in GenAI and end-to-end ML systems
Intern Machine Learning Engineer specializing in LLM agents and full-stack systems
Mid-level AI Engineer specializing in agentic workflows and RAG systems
Senior Software Engineer specializing in backend systems and LLM-powered products
Senior Machine Learning Engineer specializing in Generative AI RAG systems
Mid-level Generative AI Engineer specializing in LLM orchestration, RAG, and agentic workflows
Senior Machine Learning Engineer specializing in AI systems, LLMs, and MLOps
Rithindatta Gundu
Mid-level AI/ML Engineer specializing in LLM systems and cloud MLOps
“Built a production LLM-powered fraud detection platform at Wells Fargo, combining OpenAI/Hugging Face models with RAG-based explanations to make flagged transactions interpretable for risk and compliance teams. Delivered low-latency, real-time inference at high scale on AWS (SageMaker + EKS), with strong observability and security controls, reducing manual reviews and false positives in a regulated environment.”
AnilKumar Kanakadandila
Mid-level Data & AI Engineer specializing in data engineering, analytics, and LLM/RAG apps
“Built a production RAG-based “unified assistant” that consolidates siloed company documents into a single chatbot while enforcing fine-grained access control via RBAC/metadata filtering with OAuth2/JWT. Experienced orchestrating LLM workflows with LangChain/LangGraph + FastAPI (async + caching) and measuring performance via retrieval accuracy and response-time SLAs. Also delivered a churn analytics solution with dashboards and automated retention campaigns using n8n.”
Akshay Danthi
Senior AI Engineer specializing in production GenAI systems
“AI engineer who has shipped production LLM systems end-to-end, including a natural-language-to-SQL analytics copilot for career advisors that achieved ~95% query success through schema grounding, access controls, and automated regression testing with golden queries. Also builds LangGraph-orchestrated multi-step agents (resume analysis, recommendations) and RAG pipelines (PDF ingestion + FAISS) and partners closely with non-technical users to drive adoption and trust.”
ASHISH DONDAPATI
Mid-level AI/ML Engineer specializing in Generative AI for Financial Services
“ML/AI engineer with strong financial-services domain experience who has built production systems spanning trade anomaly detection, investment-research RAG, and agentic LLM workflows. Particularly compelling for teams needing someone who can take ML/GenAI from prototype to monitored production while balancing compliance, latency, cost, and reliability.”
Jaya Rathi
Senior Software Engineer specializing in distributed systems and cloud platforms
“Full-stack developer who built a unit billing subscription portal in React, Node.js, TypeScript, and PostgreSQL to track employee billable hours and generate paperless invoices, including BambooHR integration and async workflows with RabbitMQ. Also independently exploring agentic AI through a support desk assistant using RAG, ChromaDB, and LangChain, with a strong focus on grounding, type safety, and measurable retrieval quality.”
Jax Diagana
Senior AI Engineer specializing in forward-deployed voice agents and incident-response automation
“FDE at Bland.ai and founder of Fi (incident-response agent) who routinely takes LLM/agentic concepts from prototype to production. Has hands-on experience reverse-engineering undocumented systems to deliver integrations, building LLM testbeds for voice-agent reliability, and rapidly shipping RAG/semantic search solutions (e.g., Confluence runbooks) after deep customer discovery with DevOps/SRE teams.”
Chandra Shekar Akkandra
Mid-level AI/ML Engineer specializing in fraud detection and risk analytics in Financial Services
“Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.”