Vetted Machine Learning Engineers in the Bay Area

“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.”

“Built a production SQL chatbot for access-log analytics that replaced manual custom report requests with natural-language querying, using LangGraph and a ChromaDB-backed RAG pipeline for grounded, consistent answers. Implemented a privacy-preserving design where the LLM never sees raw customer data (only query metadata) and has experience building multi-agent/tool-calling systems with LangGraph (DeepAgents), including solving sub-agent communication drift via self-reflection.”

“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.”

“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).”

Laasya Muktevi

Screened

Intern Machine Learning Engineer specializing in forecasting, NLP, and RAG systems

San Jose, CA5y exp

“Intern who built and deployed a production LLM-powered contract analysis system for finance teams: Azure Document Intelligence for text/table extraction plus Gemini prompting to surface key terms and risks via an async API and simple UI. Emphasizes reliability in production with fallbacks, guardrails against hallucinations, and operational concerns like latency/cost/versioning, delivering summaries in under 30 seconds instead of hours.”

Rong Hu

Screened

Junior Robotics Data Engineer specializing in multi-sensor perception datasets

Milpitas, CA2y exp

“Robotics software engineer focused on perception data pipelines and multi-robot coordination. Built ROS 2 (rclpy) nodes for synchronized RGB/ToF/pose processing and scaled a perception training data generation pipeline from single-object to multi-object while preserving backward compatibility. Also has strong DevOps experience deploying containerized APIs on Kubernetes with Kustomize and automated releases via GitHub Actions.”

Aishwarya Thorat

Screened

Intern Data Scientist specializing in ML engineering and LLM agentic workflows

San Francisco, CA6y exp

“Built an agentic, multi-step LLM system that generates full-stack code for API integrations using LangChain orchestration, Pinecone/SentenceBERT RAG, and a human-in-the-loop feedback loop for iterative code refinement. Also collaborated with non-technical content writers and PMs during a Contentstack internship to deliver a Slack-based AI workflow that generates and brand-checks articles with one-click approvals.”