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Vetted Data Scientists in California
Pre-screened and vetted in California.
Mid-level Machine Learning Engineer specializing in Generative AI, NLP, and recommender systems
Rakesh Deshalli Ravi
Mid-level Data Science & AI Engineer specializing in LLMs and cloud ML platforms
“Built and deployed an LLM-powered mental health therapy assistant at AppHealth that segments users by stress level and delivers personalized, non-medical guidance. Implemented healthcare-focused safety guardrails (secondary LLM output filtering) and a multi-agent router workflow validated via statistical tests and therapist review, then scaled training/inference on AWS (EC2/Lambda/DynamoDB) with Kubernetes.”
Yashi Agarwal
Mid-level Machine Learning Engineer specializing in NLP, Generative AI, and RAG systems
“Built and deployed a production LLM-powered phone assistant for a healthcare clinic, combining streaming STT/TTS with RAG over approved clinic documents and strict safety guardrails to prevent unverified medical advice, plus seamless human handoff. Also has hands-on Apache Airflow experience building robust daily ML/data pipelines with data validation, retries/timeouts, monitoring, and metric-gated model deployment, and iterates closely with clinic staff using real call reviews.”
Allen Saunders
Senior DevOps/Solutions Engineer specializing in CI/CD, cloud platforms, and API integrations
“Solutions Architect with 5+ years leading pre- and post-sales engagements, focused on taking complex tooling from test/prototype to secure production through a structured discovery-to-deployment approach. Experienced in LLM workflow troubleshooting using tools like Langfuse/Gopher and in developer enablement via concise, hands-on workshops (e.g., Jenkins on Kubernetes at scale). Has navigated internal and external blockers to drive adoption and keep enterprise deals moving (including a Jenkins sale to Love's).”
Rohan Chodapunedi
Entry-level Data Scientist specializing in LLMs and analytics
“Built a zero-to-one AI contract/policy QA agent for compliance and data teams, with a strong emphasis on trust, traceability, and clause-level citations rather than just fluent answers. They combine full-stack product ownership with practical LLM systems design, including hybrid retrieval, structured outputs, and evaluation pipelines to improve reliability, latency, and cost.”
KHUSHBU KAKDIYA
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and cloud MLOps
“Built and deployed a production LLM/RAG system at CVS to automate clinical documents, addressing PHI compliance, retrieval accuracy, and latency; achieved a 35–40% reduction in review effort through chunking and FP16/INT8 optimization. Also has experience translating AI outputs into actionable insights for non-technical stakeholders (sports analysts).”
Amit Gangane
Junior Data Scientist specializing in agentic AI and RAG pipelines
“LLM/agentic systems builder who shipped production workflows at Angel Flight West and Eureka AI, combining LangGraph + RAG (Postgres/pgvector) with strong observability (LangSmith/Langfuse). Delivered large operational gains (address lookup cut from 10 minutes to 60 seconds; accuracy to 92%) and has a track record of quickly stabilizing customer-critical pipelines (Pydantic-enforced JSON for ETL) while partnering with sales/ops to drive adoption.”
Mid-level Machine Learning Engineer specializing in LLMs, semantic search, and distributed data pipelines
Senior AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems
Mid-level Data Scientist specializing in ML/NLP and scalable cloud data pipelines
Mid-level Machine Learning Engineer specializing in LLM chatbots and RAG systems
Senior AI Engineer & Data Scientist specializing in LLM and RAG systems
Intern Data Scientist specializing in model compression and agentic RAG systems
revanth polthi
Mid-level Data Scientist specializing in Generative AI and MLOps
“GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.”
Atharva Joshi
Mid-level GenAI Engineer specializing in RAG systems and AI agents
“LLM/agentic systems builder who has deployed production solutions for a resource management firm, using an MCP-driven architecture with Neo4j + Elasticsearch and a ChatGPT frontend to generate candidate/company “SmartPacks” and answer entity Q&A. Also built a LangGraph/LangSmith-orchestrated multi-agent workflow that automates data-infra change requests end-to-end (impact analysis, SQL + tests, and PR creation), and delivered a ~60% latency reduction through TTL-based context caching while improving accuracy via a business data dictionary.”
Varun Rao
Junior Data Scientist specializing in generative AI and RAG systems
“Data scientist at Guardian Airwaves building a RAG-powered quiz generator using Grok AI, with hands-on experience solving hard document-ingestion problems (PDFs with images/tables) via unstructured.io and LlamaIndex. Has deployed production systems on AWS EC2 and brings a pragmatic approach to agent reliability (human-in-the-loop, LLM-based eval, latency/cost metrics) while effectively translating RAG concepts to non-technical stakeholders.”
Mid-level AI Engineer specializing in Generative AI and HR Tech
Mid-level Machine Learning Engineer specializing in healthcare time-series and XAI
Mid-level Software Engineer specializing in LLM agents and distributed systems
Mid-level Machine Learning Engineer specializing in MLOps and multimodal AI