Professional Summary
Hands-on AI/ML executive with 20+ years building production systems across financial services, healthcare, and legal tech. Deep experience leading GenAI (LLMs, RAG, agentic workflows), Causal ML, and cloud-scale MLOps at enterprise level. Track record of delivering measurable outcomes—cost takeout, revenue uplift, risk reduction—through regulatory-grade AI governance, modern data platforms, and cross-functional delivery (product, risk, security, and compliance).
Core Competencies
AI Strategy & Governance
- • Enterprise AI Strategy & Roadmaps
- • KPI/ROI Frameworks
- • Model Risk & Responsible AI (policy, audit, explainability)
GenAI & Agentic Systems
- • GPT/LLaMA integration, RAG, retrieval governance
- • Evals, prompt/security hardening
- • Agentic workflows and tool use
Causal ML & Experimentation
- • ATE/CATE, uplift modeling
- • A/B & sequential testing
- • Observability and measurement
MLOps at Scale
- • MLflow/SageMaker/Databricks
- • Feature stores, CI/CD, monitoring
- • Model catalogs and registries
Data Platforms
- • Lakehouse on Azure/AWS
- • Streaming, Delta/Iceberg
- • Cost governance/FinOps
Leadership
- • Org design (up to 60 FTE/contractors)
- • Vendor management, budgeting
- • Executive communications
Professional Experience
Independent Consultant, Author
Hartford, CT- • Advised enterprises on AI transformation: ROI frameworks, build-vs-buy evaluations, use-case selection, and ROI assessments
- • Established Responsible/Explainable AI controls
- • Author, The AI Inflection Point – Volume 1: Financial Services (Infinidatum Press, 2025). Evidence-based case studies with ROI and decision frameworks. Available on Amazon.
Leading Financial Wellness Organization
Hartford, CT- • Owned enterprise AI/ML strategy and regulatory-grade delivery across retirement, wealth, and benefits lines—partnering with risk, legal, and compliance to operationalize Responsible AI policies and model risk controls
- • Delivered portfolio of 30+ production models and GenAI services; created KPI stack tying model outcomes to business P&L. Examples: 40% faster regulatory reporting cycle; 25–35% reduction in manual review for service/claims; 10–15% uplift in digital engagement
- • Modernized data & ML platform to Lakehouse (Azure + Databricks + MLflow + Delta); instituted model registry, feature store, and automated lineage/monitoring—cut model refresh time by 40%+ and infrastructure costs by five figures annually
- • Built AI Evals and Retrieval Governance for RAG/agentic apps (grounding integrity, hallucination guardrails, prompt security, PII redaction, audit logging)
- • Established GenAI Center of Enablement; trained 200+ practitioners; standardized patterns for LLM apps (prompt packs, tool use, chain reliability)
- • Quantified value realization with finance: $10M+ cumulative value through cost avoidance, retention lift, and productivity gains; published quarterly AI scorecards to exec staff
Largest Global Consulting Company
Boston, MA- • Led AI programs for banking/insurance clients: claims automation, fraud propensity, and next-best-action; moved pilots to production with CI/CD and monitoring
- • Delivered uplift models driving 3–5% conversion increase; introduced experiment design and incremental ROI measurement to executive steering groups
US-Based Digital Consulting Organization
Boston, MA- • Shaped C-suite AI roadmaps for FS clients; prioritized use-cases by ROI and feasibility; set up governance, data foundations, and delivery playbooks
US-Based Technology Consulting Organization
Hartford, CT- • Built large-scale NLP/IR systems on legal/financial corpora (NER, ranking, summarization); productionized models and search relevance pipelines
- • Optimized ingestion/annotation workflows over 100TB+ content; achieved double-digit relevance gains on editorial search tasks
- • Engineering roles across healthcare and enterprise data platforms (2005 – 2013)
Education & Research
PhD Candidate (Information Science, Causal ML)
University of Arkansas at Little Rock
M.S., Software Engineering
BITS Pilani
B.E., Mechanical Engineering
Bharathiar University
CORe Certificate
Harvard Business School Online
Technical Skills
AI & ML
PyTorch, TensorFlow, Hugging Face, MLflow, Databricks, SageMaker, DoWhy, EconML
GenAI/Agentic
GPT/LLaMA integration, RAG pipelines, LangChain/LlamaIndex, tool-use/agents, evals
Data & MLOps
Azure/AWS, Delta Lake/Iceberg, Spark, Kafka, feature stores, CI/CD, model monitoring/observability
Governance
Model risk management, explainability (SHAP/LIME), privacy/PII controls, audit trails, policy enforcement
Publications
The AI Inflection Point – Volume 1: Financial Services
Infinidatum Press, 2025
Evidence-based case studies with ROI and decision frameworks. Available on Amazon.
Causal Inference for Machine Learning Engineers
Springer, 2025
A Practical Guide to Causal ML for production systems. Available on Amazon.
Interested in Working Together?
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Explore My Work
Access tools, resources, and insights based on my experience
The AI Inflection Point
Evidence-based case studies with ROI frameworks and transformation insights.
AI Tools
Executive-level tools including ROI Calculator and Build vs. Buy Framework.
ROI Calculator
Calculate potential AI ROI using real-world benchmarks from Fortune 100 case studies.
Case Studies
Real outcomes from regulated industry clients with measurable business impact.