Vetted AI & Machine Learning Professionals in the Bay Area

“ML/GenAI engineer with strong end-to-end production ownership across predictive ML, RAG systems, and LLM routing. They pair solid platform engineering skills with measurable business impact, including 15% churn reduction, 35% support ticket deflection, 45% GenAI cost savings, and a shared inference library that cut deployment time from weeks to days.”

Vismay Patel

Screened

Senior AI & Machine Learning Engineer specializing in NLP, GenAI, and MLOps

Berkeley, CA7y exp

“ML/GenAI practitioner with healthcare domain depth who built and deployed a production cervical-cancer EMR classification system using a hybrid rules + medical BERT approach, optimized for high recall under severe class imbalance and PHI constraints. Experienced running end-to-end production ML/LLM pipelines with Apache Airflow (validation, promotion/rollback, monitoring, retraining) and partnering closely with clinicians to calibrate thresholds and implement human-in-the-loop review.”

“LLM engineer who has deployed privacy-preserving, real-time workplace risk monitoring over massive enterprise chat/email streams, tackling latency, hallucinations, and extreme class imbalance with model benchmarking, RAG + fine-tuning, and a pre-filter alerting layer. Also built an agentic legal contract drafting system (Jurisagent) using LangGraph/LangChain with deterministic multi-agent control flow, structured outputs, and reliability-focused evaluation/telemetry.”

Shanmukha Koganti

Screened

Mid-level AI/ML Engineer specializing in recommender systems and edge computer vision

Bay Area, CA6y exp

“ML/AI engineer with production experience at Shopify and Intel, building a deep learning product ranking system that lifted add-to-cart ~14% and serving real-time similarity search via FAISS+Redis under <20ms latency at massive scale. Also deployed computer vision models to 100+ retail edge locations using Docker/Ansible/k3s with zero-downtime rollouts, and applies strong MLOps practices (A/B testing, canary/shadow, observability) plus performance optimization (OpenVINO, INT8).”

Sai Dinesh Pusapati

Screened

Senior AI/ML Engineer specializing in GenAI agents and LLM workflows

San Francisco, CA6y exp

“LLM/AI engineer with production experience building a retrieval-based document intelligence system that extracts information from PDFs/emails, backed by Python + Spark pipelines. Focused on reliability and cost/latency optimization (caching, batch processing) and has hands-on orchestration experience with Airflow (sensors, retries, alerts). Also partnered with business stakeholders to deliver customer feedback classification/summarization for faster sentiment insights.”

Blake Thomas

Screened

Director-level Engineering Leader specializing in AI and EdTech platforms

San Mateo, CA21y exp

“Has been on the receiving end of a VC investment and took responsibility for significant parts of the diligence process, drawing parallels to hands-on work with security compliance and auditors. Approaches entrepreneurship and idea selection with a structured framework (leverage, resources/runway, passion) and a sustainability-first mindset around risk and personal/family well-being.”

“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%.”

“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.”