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Vetted Machine Learning Engineers in California
Pre-screened and vetted in California.
Mid-Level Software Engineer specializing in Data, ML, and LLM systems
Junior Machine Learning Engineer specializing in healthcare NLP and computer vision
Mid-level Applied Machine Learning Engineer specializing in multimodal healthcare AI
Mid-level Machine Learning Engineer specializing in deep learning and applied research
Mid-level AI Engineer specializing in autonomous agents and AI security
Mid-level Software Engineer specializing in ML, NLP, and backend systems
Mid-level AI/ML & Data Engineer specializing in MLOps and Generative AI
Junior Software Engineer specializing in AI and machine learning systems
Mid-level Machine Learning Engineer specializing in NLP, time-series forecasting, and edge AI
Mid-level Machine Learning Engineer specializing in healthcare risk prediction and GenAI
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.”
Yogita Adari
Mid-level AI Engineer specializing in generative AI, multimodal evaluation, and agentic RAG 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.”
Rohan Karle Sudarshan
Mid-level Software Engineer specializing in AI, backend systems, and data platforms
“Built and shipped production AI features for Aiden, including a natural-language agent and a Knowledge Hub ingestion/retrieval system. Stands out for hands-on debugging of real LLM production issues across providers like OpenAI and AWS Bedrock, improving reliability and achieving 90% response/retrieval consistency through direct LiteLLM integration, validation, monitoring, and async system design.”
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).”
Venkatarama Sai Teja Dasarathi
Mid-level Machine Learning Engineer specializing in deep learning and generative AI
“ML/NLP engineer with hands-on experience building production systems for unstructured insurance claims and customer data linking. Delivered measurable impact at scale (millions of documents), combining transformer-based NLP, vector search (FAISS/Pinecone), and human-in-the-loop validation, and has strong production workflow/observability practices (Airflow, AWS Batch, Grafana/Prometheus).”
Manvi Panjwani
Mid-level Machine Learning Engineer specializing in cloud, governance automation, and distributed systems
“Governance engineer intern at GSK who built policy-as-code automation using Open Policy Agent/Rego integrated into GitHub CI/CD and Terraform workflows. Also built and shipped a voice-enabled expense tracking app using speech-to-text + LLM structured extraction with strong validation, retries, and semantic guardrails, and designed the supporting PostgreSQL data model with performance-focused indexing.”