I help organizations translate business objectives into implementable AI systems with clear architectural boundaries and phased execution plans. My work includes defining data requirements, evaluating feasibility under existing infrastructure constraints, and identifying where statistical ML, LLM systems, or deterministic logic are most appropriate. I structure initiatives to balance time-to-value with long-term maintainability, incorporating cost modeling, latency considerations, and governance requirements early in the design process. Roadmaps include dependency sequencing, risk identification, and production-readiness criteria to prevent stalled prototypes. Where needed, I guide build-versus-buy decisions and managed model integration strategies. The result is a technically grounded plan that aligns product ambition with operational reality.
Representative Work
- Developed a multi-year AI and data roadmap for an e-commerce portfolio firm, outlining phased implementation of analytics and predictive systems based on existing data maturity and infrastructure constraints.
- Defined architecture and execution plans for enterprise analytics platforms, sequencing ingestion, modeling, and deployment milestones to reduce delivery risk.
- Advised stakeholders on LLM integration strategies, evaluating feasibility, governance implications, and infrastructure readiness prior to production deployment.
Core Technologies
AI architecture planning; data maturity assessment; build-versus-buy analysis; phased implementation strategies; cost and latency modeling; governance integration; dependency sequencing; risk assessment frameworks; production-readiness criteria; foundation model integration strategy.
I evaluate existing data and ML ecosystems to determine whether they are structurally prepared for reliable, production-grade AI deployment. Reviews focus on data quality, schema stability, pipeline integrity, model lifecycle controls, and service topology. I identify architectural bottlenecks, technical debt patterns, and integration risks that could undermine ML or LLM initiatives at scale. Where systems are immature, I outline incremental remediation strategies that improve reliability without forcing full rewrites. Assessments also include inference latency considerations, deployment workflows, and monitoring coverage relative to business-critical requirements. The goal is to provide a clear, technically grounded path from current state to production viability.
Representative Work
- Conducted architecture assessments for organizations attempting to integrate LLM and analytics features into legacy service stacks, identifying schema inconsistencies and deployment risks prior to release.
- Reviewed cloud and backend configurations to improve pipeline reliability, reduce latency variance, and formalize model promotion workflows.
- Delivered technical readiness reports outlining phased improvements to ingestion, validation, and monitoring layers before scaling ML features.
Core Technologies
Architecture auditing; data quality assessment; schema stability analysis; ML lifecycle evaluation; inference latency profiling; deployment workflow analysis; monitoring and observability review; technical debt identification; remediation roadmapping; production-readiness evaluation frameworks.
I design evaluation and governance frameworks that ensure AI systems behave predictably under real-world constraints. My work includes defining measurable performance criteria, structured validation layers, and deterministic safeguards for both statistical models and LLM-driven workflows. I implement evaluation pipelines that combine quantitative metrics with task-specific validation strategies, including retrieval grounding checks and tool-based verification. Risk controls are embedded into deployment processes through gated releases, audit logging, and traceability standards. I also address bias, interpretability, and human-in-the-loop design where outputs influence high-stakes decisions. The objective is to reduce operational, reputational, and compliance risk while maintaining system performance and velocity.
Representative Work
- Implemented deterministic validation layers and tool-based verification to eliminate hallucinations in accuracy-critical LLM analytics workflows.
- Designed evaluation frameworks for predictive and diagnostic ML systems, aligning model thresholds with business risk tolerances prior to release.
- Established observability and tracing standards for generative systems, enabling structured review of outputs and rapid failure isolation.
Core Technologies
Evaluation pipeline design; metric definition and thresholding; deterministic validation frameworks; retrieval grounding checks; bias and interpretability analysis; audit logging; deployment gating strategies; human-in-the-loop integration patterns; LLM tracing and observability; risk modeling and mitigation frameworks.
I translate complex AI systems into clear, decision-ready artifacts for executive and cross-functional stakeholders. My work includes producing architecture documentation, technical justifications, cost models, and phased implementation briefs that align engineering constraints with business objectives. I facilitate workshops and technical briefings that improve organizational literacy around ML, LLM systems, and data platform maturity. Where funding or external validation is required, I develop technically grounded proposals that connect system design to measurable outcomes. Communication is structured to preserve technical accuracy while enabling informed decision-making at the leadership level. The result is organizational alignment around feasible, governed, and strategically sequenced AI initiatives.
Representative Work
- Developed executive-facing architecture briefs and multi-phase AI investment plans supporting long-term analytics and automation initiatives.
- Produced structured technical documentation and cost-performance analyses used to secure funding for ML-driven product expansion.
- Delivered workshops and stakeholder sessions translating ML system behavior, risk considerations, and deployment tradeoffs into operational decision frameworks.
Core Technologies
Technical architecture documentation; executive brief development; cost and performance modeling; phased implementation planning; stakeholder workshops; funding and proposal development; cross-functional alignment facilitation; AI literacy enablement; risk communication frameworks.