.png){kind=logo}
Vetted Machine Learning Engineers in California
Pre-screened and vetted in California.
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Staff AI/ML Engineer specializing in LLMs, fraud detection, and MLOps
Esha Pahwa
Intern Machine Learning Engineer specializing in LLM agents and multimodal reasoning
“LLM/agent engineer who built a production code-generation agent at Corvic AI that lets non-technical users query CSV/tabular data in natural language by generating and executing Python. Focused on making LLM systems reliable and scalable via schema-aware validation, sandboxed execution-feedback retries, prompt caching/embeddings, async execution, and high-throughput data processing with Polars; also partnered with Adobe product/marketing to ship brand-aligned AI content generation for email and push notifications.”
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Senior AI Full Stack Engineer specializing in generative AI and ML systems
Junior Machine Learning Engineer specializing in fraud detection and healthcare ML
Jayaprakash Majji
Mid-level AI/ML Engineer specializing in LLM fine-tuning, RAG, and scalable inference
“ML/LLM engineer who built and shipped an LLM-powered internal knowledge assistant at Meta, focusing on production-grade RAG to reduce hallucinations and improve trust. Deep experience with scaling and serving (FSDP/DeepSpeed/LoRA, Triton, Kubernetes autoscaling) and reliability practices (Airflow retraining, MLflow versioning, monitoring with rollback), including sub-100ms latency and ~35% GPU memory reduction.”
Vishanth Iyer
Senior AI/ML Engineer specializing in LLMs, multimodal AI, and scalable MLOps
“ML/NLP engineer with experience at NVIDIA and Cruise building production-grade AI systems across genomics/biomedical research and autonomous vehicle data. Has delivered multimodal LLM pipelines, large-scale entity resolution, and hybrid semantic search (BERT embeddings + FAISS + Elasticsearch), with measurable impact (≈40% accuracy/retrieval gains; ≈30% data consistency improvement) and strong MLOps practices (Kubernetes, CI/CD, MLflow, Prometheus/Grafana).”
Senior AI/ML Engineer specializing in LLMs, RAG, and multimodal systems
Senior Machine Learning Engineer specializing in LLMs and scalable MLOps
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level AI/ML Engineer specializing in generative AI, LLMs, and MLOps
Mid-level Applied AI Engineer specializing in LLMs, MLOps, and real-time AI systems
Mid-level AI/ML Engineer specializing in LLMs, multilingual NLP, and low-latency MLOps
Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems
Ahmed Sadaqat
Senior Machine Learning Engineer specializing in production ML and predictive analytics
“ML/AI engineering leader who has owned end-to-end production systems from experimentation through deployment, monitoring, and iteration at meaningful scale. They describe running a 1M+ records/day prediction platform with 99.9% availability, shipping a RAG-based conversational AI feature for 50,000 active users, and consistently improving precision, latency, reliability, and cost with measurable business impact.”
Subhash Mothukuru
Mid-level Machine Learning Engineer specializing in LLMs, generative AI, and MLOps
“Built and shipped a production LLM-powered medical scribe that generates structured clinical visit summaries using RAG, strict JSON schemas, and post-generation validation to reduce hallucinations. Experienced in making LLM workflows deterministic and observable (structured logging/metrics/tracing) and in evaluation-driven iteration with metrics like schema pass rate and edit rate; collaborated closely with clinicians and policy stakeholders at Scale AI to drive adoption.”
Sahil Bansal
Mid-level Backend & ML Engineer specializing in LLM systems and scalable AI pipelines
“Built and shipped a real-time AI phone agent for small businesses that handles bookings/FAQs/messages using streaming ASR, an LLM with tool-calling, and TTS; deployed to production for multiple paying customers. Demonstrates strong applied LLM reliability practices (tool-first grounding, retrieval, hard-negative testing, and production monitoring) and experience orchestrating multi-step AI workflows with Airflow, Prefect, and AWS Step Functions.”
Senior Software Engineer specializing in AI/ML and cloud infrastructure
Junior Machine Learning Engineer specializing in LLMs, data pipelines, and MLOps
Mid-level Machine Learning Engineer specializing in search ranking and NLP
Mid-level AI/ML Engineer specializing in GPU-accelerated LLMs, RAG, and production MLOps