.png){kind=logo}
Vetted AI Engineers in California
Pre-screened and vetted in California.
Senior Software Engineer specializing in Python AI/ML integration and experimentation pipelines
Junior Software Engineer specializing in AI infrastructure and applied machine learning
Chandra sai kiran Kammari
Mid-level Machine Learning Engineer specializing in fraud detection and real-time personalization
“ML/LLM engineer with Stripe and Adobe experience who productionized a transformer-based Payments Foundation Model for real-time fraud detection at global scale (billions of transactions). Built petabyte-scale ETL/feature pipelines (Spark/EMR, Airflow, dbt, Kafka/Flink) and achieved <100ms multi-region inference (EKS, TorchServe, edge/Lambda, GPU/CPU routing) with strong PCI-DSS/GDPR compliance and explainability (SHAP/LIME), reporting a 64% fraud accuracy improvement.”
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and MLOps
Mid-level AI Engineer specializing in LLM applications and personalization
Mid-level AI/ML Engineer specializing in LLM RAG pipelines and cloud MLOps
Param Yanamandra
Staff/Lead Software Architect specializing in Contact Center platforms and GenAI automation
“Built and deployed production LLM systems in healthcare and at LinkedIn: automated pen-and-paper clinical trial evaluations with a 40x efficiency gain and created an evidence-based Evaluation Agent focused on accuracy and speed. Also used Temporal to orchestrate resilient data-ingestion workflows for customer support staffing prediction, improving prediction outcomes by 40% while handling missing data, retries, and backfills.”
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Principal Salesforce Architect specializing in AI, AWS, and enterprise integrations
Mid-level AI Engineer specializing in LLMs, RAG chatbots, and cloud AI testing
Leela Tikkisetty
Mid-level Software Engineer specializing in ML platforms and cloud-native backend systems
“Software engineer with experience at Google and the City and County of San Francisco building production AI systems, including a RAG-based internal support chatbot and ML-driven ticket priority tagging. Has scaled data/ML platforms with Airflow on GCP (1M+ records/day, 99.9% SLA) and deployed multi-component systems with Docker and Kubernetes (GKE), using modern LLM tooling (LangChain/CrewAI, Claude/OpenAI, Pinecone/ChromaDB, Bedrock/Ollama).”
Junior AI/Software Engineer specializing in LLMs, NLP, and cloud infrastructure
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Junior AI Engineer specializing in computer vision, LLM infrastructure, and generative media pipelines
Mid-level AI Engineer specializing in LLM agents and applied machine learning
Aashna Kunkolienker
Junior AI Engineer specializing in agentic workflows and ML platforms
“Building a production LLM/agent system for a leading US dental provider that extracts rules from payer handbooks/portals and EDI 271 responses to validate and improve patient cost estimates. Combines GCP stack (BigQuery, GKE, Cloud Run, Pub/Sub, Vertex AI) with strong agent reliability practices (observability, validator agents, grounding, PII/hallucination guardrails, confidence scoring) and has led non-technical customer stakeholders on enterprise ServiceNow↔Aha sync and AI-powered enterprise search/summarization.”
Ajay Madhusudhan Thumala
Junior Software Engineer specializing in data engineering and LLM applications
“Computer science engineer and master’s graduate who independently built a mechatronics-heavy capstone prototype: a smartphone concept for deafblind users using micro-actuator arrays for braille reading. Also has platform engineering experience at Quantiphi, deploying webhooks to Kubernetes and implementing GitOps-based CI/CD using AWS CodeCommit/CodeBuild and ECR.”
Ishan Kumar Anand
Junior AI/ML Engineer specializing in multimodal generative models and NLP
“AI/ML engineer who has built a production text-to-image generation system in PyTorch with an AWS-backed inference setup, focusing on GPU-efficient training and embedding-space architectural choices inspired by recent research (e.g., Meta VL-JEPA). Uses both metric-based evaluation (FID) and human testing to validate real-world visual quality, and can translate technical concepts for non-technical stakeholders.”
Junior AI Engineer specializing in GenAI, RAG, and agentic systems
Mid-level AI/ML Engineer specializing in LLM pipelines, RAG systems, and agentic automation
Entry AI Application Engineer specializing in GPU infrastructure benchmarking
Mid-level AI/ML Engineer specializing in GenAI, RAG, and multi-agent systems