Browse Skills

Guides feature engineering for ML models including feature store design (Feast, Tecton), feature selection methods, transformation pipelines, encoding strategies, feature importance analysis, and automated feature engineering techniques.

Conducts comprehensive bias audits and ethical assessments of AI/ML systems, applying fairness metrics, regulatory frameworks (NIST AI RMF, EU AI Act), and structured impact assessments to identify and mitigate algorithmic harms.

Design and implement retrieval-augmented generation systems for legal documents with citation-safe retrieval, hallucination prevention, and privilege-aware document handling.

Senior MLOps drift detection specialist that designs production-grade monitoring pipelines with statistical tests, alerting thresholds, and automated retraining triggers.

Design and deploy predictive maintenance systems using edge computing, IoT sensor data, and machine learning models for anomaly detection and remaining useful life estimation in industrial environments.

Evidence-based framework that classifies AI-assisted coding practices into High-Leverage, Context-Dependent, and Dangerous tiers with a rigorous 4-criteria evaluation rubric.

Diagnoses and resolves neural network training failures including gradient issues, loss plateaus, overfitting, and suboptimal hyperparameters. Provides systematic debugging workflows with actionable fixes.

Design and implement an AI governance council structure that provides organizational oversight for responsible AI development, deployment, and monitoring aligned with emerging regulations and industry frameworks.

Architects multimodal AI systems that combine vision, language, and audio modalities. Designs cross-modal fusion strategies, selects appropriate models (CLIP, GPT-4V, Gemini), and builds end-to-end multimodal pipelines.

Expert in AI cost optimization — token efficiency, model routing, prompt caching, GPU cost management, and inference optimization strategies for LLMs and ML systems.

Designs, builds, and optimizes complex multi-step Zapier workflows connecting multiple SaaS applications, with branching logic, error handling, sub-Zaps, and task optimization for production-grade business automation.

Designs and implements graph neural network architectures for relational and structured data problems. Covers GCN, GAT, GraphSAGE, knowledge graphs, message passing, and graph-level tasks including node classification, link prediction, and graph generation.

Optimizes few-shot prompting strategies for large language models by selecting, ordering, and formatting demonstration examples that maximize output quality while minimizing token usage and cost.

Expert in PyTorch production systems — neural network architecture, training loops, CNNs, distributed training, and model optimization for deployment.

Guides practitioners through end-to-end LoRA and QLoRA fine-tuning workflows, from dataset curation and hyperparameter selection to training optimization, evaluation, model merging, and deployment of parameter-efficient fine-tuned language models.

Expert in supervised learning pipelines — classification, regression, model selection, hyperparameter tuning, and production deployment with scikit-learn, XGBoost, and LightGBM.

Designs automated machine learning pipelines for model selection, hyperparameter optimization, and neural architecture search. Covers AutoML frameworks (Optuna, Ray Tune, Auto-sklearn), search strategies, and production integration.

Design and implement safety, quality, and compliance guardrail systems for LLM-powered applications, covering input validation, output screening, prompt injection defense, PII protection, and regulatory compliance mapping.

Design effective chain-of-thought prompting strategies that elicit step-by-step reasoning from large language models for complex problem-solving, analysis, and decision-making tasks.

Designs and optimizes vector embedding systems for semantic search, similarity matching, and retrieval applications. Covers embedding model selection, dimensionality reduction, vector database architecture, and search quality optimization.

Designs comprehensive LLM prompt evaluation frameworks with tailored metric suites, automated eval pipelines, A/B testing methodology, red-teaming protocols, and regression detection systems using industry-standard tools like RAGAS, DeepEval, and promptfoo.

Expert in AI safety evaluation — adversarial testing, jailbreak prevention, prompt injection defense, bias detection, and safety benchmarking for LLMs and AI systems.

Expert guidance for selecting, fine-tuning, and deploying transformer-based NLP models using the Hugging Face ecosystem and PyTorch for production natural language processing tasks.

Provides expert guidance on transformer neural network architectures, including attention mechanisms, positional encoding schemes, encoder-decoder design patterns, tokenization strategies, and context window optimization for building and understanding modern LLMs.