.png){kind=logo}
Vetted Machine Learning Engineers in California
Pre-screened and vetted in California.
Junior Software Engineer specializing in DevOps/SRE and ML platform infrastructure
Mid-level Software Engineer specializing in backend systems and LLM-powered applications
Senior Data Scientist / AI & ML Engineer specializing in agentic AI and generative AI
Mid-level Full-Stack Software Engineer specializing in ML platforms and observability
Mid-level Machine Learning Engineer specializing in LLM training and FinTech ML systems
Mid-level AI/ML Engineer specializing in LLMs, NLP, and predictive analytics for finance
Mid-level AI/ML Engineer specializing in MLOps, real-time data platforms, and generative AI
Mid-level AI/ML Engineer specializing in NLP, RAG, and cloud MLOps
Junior AI Engineer & Data Scientist specializing in GenAI and Computer Vision
Mid-level Machine Learning Engineer specializing in GenAI and end-to-end ML systems
Senior Data Scientist / ML Engineer specializing in computer vision and production ML systems
Mid-level AI/ML Developer specializing in LLMs, RAG, and data pipelines
Mid-level AI/ML Engineer specializing in GenAI, RAG, and multi-agent systems
Senior Machine Learning Engineer specializing in Generative AI RAG systems
Mid-level AI/ML Engineer specializing in LLMs, forecasting, and MLOps deployment
Mid-level AI/ML Engineer specializing in LLMs, NLP, and scalable ML pipelines
Srinivas Tenneti
Mid-level AI/ML Engineer specializing in GenAI and predictive modeling
“Built and deployed a GPT-4-powered medical assistant for clinical staff to reduce time spent searching guidelines and EHR information, with a strong emphasis on safety and compliance. Uses strict RAG, confidence thresholds, and fallback behaviors to prevent hallucinations, and runs production-grade workflows orchestrated with LangChain/LangGraph plus Docker/Kubernetes/MLflow and monitoring for reliability and cost.”
Prithviraju Venkataraman
Mid-level AI/ML Engineer specializing in MLOps, NLP, and Computer Vision
“Built and deployed a production LLM-powered text extraction/classification system that converts messy unstructured reports into searchable insights, running on AWS SageMaker with automated retraining and monitoring. Strong in orchestration (Step Functions/Kubernetes/Airflow patterns) and reliability practices (gold datasets, prompt/tool unit tests, shadow/canary/A-B testing, guardrails/rollback), and has experience translating non-technical stakeholder needs into an NLP workflow plus dashboard.”
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.”