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Vetted AI & Machine Learning Professionals in the Bay Area
Pre-screened and vetted in the Bay Area.
Popular Titles
Mid-Level Software/ML Engineer specializing in cloud-native ML systems
Senior AI Solutions Engineer specializing in LLM-powered enterprise systems
Senior Product & Solutions Leader specializing in AI platforms, data products, and FinTech
Junior Software Engineer specializing in Generative AI and full-stack development
Entry-level Machine Learning Engineer specializing in LLM systems and RAG
Entry-level NLP/LLM Researcher specializing in multilingual evaluation and retrieval
Entry-Level Software Engineer specializing in ML and Full-Stack Development
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI/ML Engineer specializing in NLP, RAG, and computer vision
Mid-level AI/ML Engineer specializing in GenAI, MLOps, and anomaly detection
Intern software engineer specializing in AI and full-stack systems
Senior Computer Vision Engineer specializing in medical imaging and MLOps
Senior Machine Learning Engineer specializing in MLOps and Generative AI
Senior AI Python Engineer specializing in Generative AI and MLOps
Saikrishna Paila
Junior AI Engineer specializing in RAG pipelines and agentic AI systems
“Built and shipped production RAG/agentic systems in high-stakes domains (biomedical and legal), including an enterprise biomedical document retrieval platform over ~10k scientific docs and a multilingual African-law assistant at the World Bank. Deep hands-on experience with LangChain/LangGraph/LlamaIndex and evaluation tooling (LLM-as-a-judge, safety/hallucination detection), with measurable gains in retrieval quality and hallucination reduction.”
Likith Kumar Tarala
Mid-level Machine Learning Engineer specializing in deep learning and generative AI
“AI/ML engineer who has deployed transformer-based NLP systems to production via Python REST APIs and Kubernetes on AWS/Azure, with a strong focus on latency optimization (p95), reliability, and scalable orchestration. Demonstrates pragmatic model tradeoff decision-making and strong stakeholder collaboration—improving adoption by making outputs more actionable with summaries, extracted fields, and confidence indicators.”
Yogita Adari
Mid-level AI Engineer specializing in generative and multimodal systems
“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”
Kanav Chopra
Senior Front-End Engineer specializing in React, TypeScript, and healthcare AI
“Frontend-focused engineer building production-grade, browser-based AI products, including a live clinical decision support platform for Stanford clinicians. Stands out for combining sophisticated React/TypeScript streaming UI architecture with strong browser performance expertise, including Core Web Vitals and rendering-pipeline optimization.”
Harsh Tripathi
Mid-level Machine Learning Engineer specializing in LLMs, agentic AI, and risk/fraud modeling
“Built and productionized an agentic LLM workflow during a summer internship to transform unstructured clinical reports into analytics-ready structured data, using a LangChain multi-agent design plus an LLM-as-a-judge layer to control quality in a regulated setting. Also has experience orchestrating ML pipelines at Piramal Capital using AWS Step Functions/EventBridge/CloudWatch, with strong emphasis on observability, evaluation rigor, and measurable impact (80–90% reduction in manual data entry).”
Hemanth Kumar Gajagiri
Mid-level Full-Stack AI Engineer specializing in agentic systems and scalable platforms
“AI-focused full-stack/DevOps engineer who goes beyond using copilots and has built production-oriented LLM systems such as natural-language-to-SQL and structured insight extraction pipelines. Stands out for treating AI as an accelerator rather than a replacement, with a strong emphasis on guardrails, validation, observability, and safe deployment practices in agent-based and distributed systems.”
Suraj Thangellapally
Junior Software Engineer specializing in machine learning and data science
“Python backend engineer who built a personal LLM-powered AI code review tool that parses code into context-preserving diff chunks and uses the OpenAI API to analyze and summarize changes. Has hands-on Kubernetes deployment experience (replicas, rolling updates, ConfigMaps/Secrets, health probes) and follows GitOps-style, declarative CI/CD workflows; also has experience designing streaming/event-style processing with attention to reliability and observability.”
Somil Shah
Mid-level AI/ML Engineer specializing in generative AI, RAG platforms, and LLM agents
“AI/LLM engineer who has shipped 10+ production applications, including InvestIQ on GCP—a production-grade RAG due-diligence engine that ethically scrapes web/PDF sources, builds a ChromaDB knowledge base, and delivers analyst-style dashboards plus a citation-backed chat copilot. Deep focus on reliability (evidence-only answers, hard citations, refusal gating), retrieval tuning, and orchestration (Airflow/Cloud Composer), plus multi-agent systems (CrewAI with 7 specialized finance agents).”