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Vetted Machine Learning Engineers in California
Pre-screened and vetted in California.
SHREY MATHUR
Mid-level Machine Learning Engineer specializing in LLMs and AI products
“Applied ML/LLM engineer currently building AppleCare’s production chat recommender, owning the full lifecycle from transcript cleaning and fine-tuning through distributed deployment, monitoring, and iterative improvement. Their work delivered >10% copy-count improvement, 5% lower modification rate, 60% cost reduction, and $1.1M profitability in 2025, and they also created a reasoning-data generation approach that enabled a reasoning model and a judge model that cut eval time by over 99%.”
Vishnu Varma
Senior AI/ML Engineer specializing in LLMs, GenAI, and MLOps
“AI/ML engineer (Cognizant) who built a production, real-time credit card fraud detection platform combining deep-learning anomaly detection with an LLM-based explanation layer. Strong focus on regulated deployment: addressed class imbalance and feature drift, and added guardrails (SHAP/structured inputs, fine-tuning on analyst reports, rule-based validation) to keep explanations accurate and compliant. Orchestrated the full pipeline with Airflow + Databricks/Spark and used MLflow/Prometheus plus A/B and shadow deployments for measurable reliability.”
Mid-Level Software Engineer specializing in ML platforms and full-stack systems
Mid-level Machine Learning Engineer specializing in computer vision and LLM systems
Mid-level AI/ML Engineer specializing in LLMs and document intelligence
Mid-level AI/ML Engineer specializing in LLM agentic systems and MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production MLOps
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Mid-level AI/ML Engineer specializing in GenAI, LLMs, and real-time ML systems
Mid-level AI/ML Engineer specializing in NLP, RAG, and agentic AI
Max Matkovski
Junior Machine Learning Engineer specializing in data pipelines and applied AI
“Built a production AI agent for phishing fraud detection using n8n orchestration, Claude (Sonnet 4/MCP), VirusTotal, and JavaScript formatting to generate and deliver email-based reports via Gmail. Has experience evaluating detection accuracy against known examples, iterating via feedback, and presenting AI solutions to non-technical teams.”
Alp Komban
Junior Machine Learning Engineer specializing in computer vision for medical imaging
“Applied ML/LLM practitioner working in healthcare-facing products, using RAG and LoRA fine-tuning on medical data and implementing production monitoring (confidence scoring) for clinician oversight. Has hands-on experience debugging agentic/LLM pipelines (including OCR preprocessing fixes) and regularly delivers technical demos to doctors, investors, and conferences—contributing to adoption and even helping close a funding round through end-to-end pipeline walkthroughs.”
Haider Shah
Principal AI/ML Architect specializing in GenAI, LLMs, RAG, and Agentic AI
“FinTech/AI engineer who has shipped an end-to-end discrepancy-detection product for financial managers using Next.js, FastAPI/GraphQL, Pinecone, and AWS (with dev/staging/prod, observability, A/B testing, and documentation). Also built an AI-native “AI Genesis” system with agentic cyclic workflows, routing, and tool use, and has experience modernizing legacy systems via the strangler fig pattern while coordinating with senior stakeholders on a 5G autonomous simulation platform.”
Staff Machine Learning Engineer specializing in LLM agents and ML systems
Junior ML Engineer specializing in GenAI agents, RAG, and computer vision
Mid-level GenAI & Analytics Engineer specializing in LLM and cloud cost/finance analytics
Senior Data Scientist specializing in LLMs, NLP, and anomaly detection
Senior AI/ML Engineer specializing in Generative AI and Computer Vision
Omkar Bhope
Staff Full-Stack Engineer specializing in AI platforms and infrastructure automation
“Backend/full-stack engineer building complex internal platforms and customer-facing demos at the intersection of infrastructure and product. Shipped a no-code Product Lifecycle Manager for manufacturing (3 manufacturers, 1000+ evolving tests) using AWS S3/SQS ingestion and extensible Postgres (EAV+JSONB) with end-to-end traceability. Also built a FastAPI-based company data intelligence platform with Okta-secured RBAC and an LLM/MCP layer for ChatGPT-like analytics over enterprise data sources.”
Raja Gurugubelli
Mid-level GenAI Engineer specializing in production RAG and LLM fine-tuning
“LLM engineer who built a production seller-support RAG system at eBay using hybrid retrieval (BM25 + Pinecone vectors) with Cohere reranking, LangGraph orchestration, and citation-grounded answers. Strong focus on reliability: semantic/structure-aware chunking, automated Ragas-based evaluation with nightly regressions, and production observability (LangSmith) plus drift monitoring (Arize). Also implemented a multi-agent fraud pipeline with AutoGen using JSON-schema contracts and explicit termination conditions.”