Senior AI Engineer/Architect & Full Stack Data Scientist specializing in enterprise-scale distributed systems, real-time machine learning, and production generative AI architectures. Proven track record of designing fault-tolerant systems processing 24+ billion records with sub-200ms latency, delivering measurable business impact including $122.9M+ in cost savings through ML-driven optimization.

• Stream Processing: Apache Kafka, AWS Kinesis, Event-driven architectures at petabyte scale
• Data Orchestration: Apache Airflow, Prefect, complex DAG management for ML pipelines
• Storage Systems: Delta Lake, Apache Hudi, Snowflake, distributed data lake architectures
• Compute Frameworks: Apache Spark, Databricks, auto-scaling cluster management

• Bayesian Methods: MCMC sampling, Hierarchical modeling, Probabilistic programming with Stan/PyMC
• Causal Inference: Propensity score matching, Instrumental variables, Difference-in-differences analysis
• Survival Analysis: Cox regression, Kaplan-Meier estimation, Time-to-event modeling at scale
• Time Series: ARIMA/GARCH models, State-space modeling, Multivariate forecasting frameworks

• Feature Engineering: Automated feature selection, Principal components, Statistical transformations
• Model Development: Ensemble methods, Cross-validation strategies, Hyperparameter optimization
• MLOps Integration: Model versioning, A/B testing frameworks, Continuous model monitoring
• Deployment Architecture: Real-time inference APIs, Batch prediction pipelines, Edge deployment

• Randomized Experiments: Multi-armed bandits, Sequential testing, Adaptive experimental design
• Observational Studies: Natural experiments, Regression discontinuity, Synthetic control methods
• Executive Analytics: KPI frameworks, Performance dashboards, Strategic metric design
• Data Visualization: Interactive dashboards, Statistical graphics, Executive reporting systems

• Model Lifecycle: MLflow, Weights & Biases, automated A/B testing and model versioning
• Production ML: Real-time inference, model serving at scale, latency optimization (<200ms)
• Feature Engineering: Real-time feature stores, statistical transformations, ensemble methods
• AutoML & Hyperparameter Optimization: Optuna, Ray Tune, distributed hyperparameter search

• LLM Engineering: Fine-tuning, RLHF, prompt engineering, context optimization
• RAG Architectures: Vector databases, embedding pipelines, semantic search at scale
• Multi-Agent Systems: LangChain, LangGraph, complex reasoning workflows
• Model Integration: OpenAI, Anthropic Claude, HuggingFace Transformers, custom model deployment

• AWS Expertise: SageMaker, Bedrock, Lambda, Step Functions, EventBridge
• Infrastructure as Code: Terraform, CloudFormation, automated provisioning
• Containerization: Docker, Kubernetes, EKS, container orchestration at scale
• CI/CD: Jenkins, GitHub Actions, automated testing and deployment pipelines

graph TB
    A[Enterprise AI] --> B[Distributed ML]
    A --> C[Real-time Inference]
    A --> D[Cost Optimization]
    
    E[Generative AI] --> F[RAG Systems]
    E --> G[Multi-Agent AI]
    E --> H[LLM Fine-tuning]
    
    style A fill:#FF6B6B
    style E fill:#4ECDC4

graph TB
    A[Statistical Methods] --> B[Survival Analysis]
    A --> C[Bayesian Inference]
    A --> D[Time Series]
    
    E[Advanced Analytics] --> F[Causal Inference]
    E --> G[Predictive Modeling]
    E --> H[Anomaly Detection]
    
    style A fill:#DDA0DD
    style E fill:#20B2AA

graph TB
    I[ML Pipeline] --> J[Feature Engineering]
    I --> K[Model Selection]
    I --> L[Deployment]
    
    M[Business Intelligence] --> N[KPI Dashboards]
    M --> O[Executive Reporting]
    M --> P[Data Visualization]
    
    style I fill:#FF69B4
    style M fill:#87CEEB

graph TB
    Q[Experimental Design] --> R[A/B Testing]
    Q --> S[Randomized Trials]
    Q --> T[Power Analysis]
    
    U[Infrastructure] --> V[Cloud Architecture]
    U --> W[DevOps Pipeline]
    U --> X[Monitoring]
    
    style Q fill:#FFB347
    style U fill:#98FB98

Current Focus Areas:

• Distributed ML Systems: Optimizing training and inference across multi-cloud environments with 99.9% uptime
• Real-time AI: Sub-100ms latency requirements for financial and healthcare applications achieving 95%+ accuracy
• Advanced Analytics: Bayesian inference, survival analysis, and causal modeling for predictive insights with 85%+ precision
• Statistical Computing: Time-series forecasting, A/B testing frameworks, and experimental design reducing decision uncertainty by 70%
• LLM Optimization: Custom fine-tuning and RLHF for domain-specific applications improving response quality by 40%
• Cost Engineering: ML workload optimization reducing infrastructure costs by 60-80% while maintaining performance
• AutoML Pipelines: End-to-end automated model lifecycle management with 90%+ deployment success rate

Key Achievements:

• Technical Leadership: Led architecture decisions for systems processing 120 petabytes annually
• Research Contributions: Published predictive maintenance algorithms with 64.53% accuracy improvement
• Business Impact: Delivered quantifiable ROI through ML-driven operational optimization
• Industry Recognition: Invited speaker at ML conferences and technical advisory boards