Hemant Verma

About Hemant Verma

I design and review production-grade LLM architectures for enterprises and founders building RAG-based and domain-specific AI systems in regulated or high-stakes environments.My work focuses on end-to-end LLM systems—from data readiness and retrieval design to model orchestration, guardrails, explainability, and failure-mode handling—so AI solutions are auditable, reliable, and safe to scale beyond demos.Many organizations know they need LLMs but struggle with questions like:• How do we architect RAG systems that don’t hallucinate?• How do we combine deterministic logic with LLM reasoning?• How do we make AI outputs explainable and defensible?• How do we deploy LLMs without creating compliance or operational risk?That’s where I help.What I specialize in:• LLM & RAG architecture (retrieval pipelines, embeddings, evaluation loops)• Model orchestration and routing (local vs cloud, cost/latency tradeoffs)• Deterministic + LLM hybrid systems for compliance-critical workflows• Explainable, governance-first AI systems• Domain-specific model adaptation strategiesHow I work:I approach AI as a system, not a feature. That means clear architectural contracts, explicit failure modes, strong data foundations, and governance built in—not bolted on later.I work through clear, written architectural contracts (RFCs) that define scope, assumptions, constraints, and decision rationale. This ensures alignment, reduces rework, and allows LLM systems to be designed, reviewed, and evolved with confidence—especially in regulated or high-risk environments. I often engage as a fractional LLM architect and advisor, helping teams move fast without cutting corners.Who I work with:• Founders and CTOs building AI-first products• Enterprises deploying LLMs in regulated domains• Data and engineering leaders who need a clear LLM architecture blueprintEngagements:I’m open to LLM architecture design, architecture reviews, and advisory engagements—especially for teams building RAG systems, domain-adapted models, or compliance-aware AI platforms.Most teams only realize these issues after pilots stall or production risks surface — when fixes are far more expensive.If you’re serious about deploying LLMs that your business—and regulators—can trust, let’s connect.I am available for short-term LLM architecture reviews and GenAI advisory engagements: https://calendly.com/ai_data_strategy/15min

Experience

1. Head of Data Architecture

   Kessel Run

   Jan 2025 — Present · Boston, MA, US

   Designed enterprise data foundations enabling analytics, AI, and GenAI adoption at scale- Defined data architecture and governance standards supporting explainable and compliant AI systems- Integrated data pipelines, metadata, and quality frameworks to support AI-readiness- Acted as senior advisor on data and AI architecture decisions

Education

• Institute For Technology and Management

  Master of Science - MS, Computer Science

• Institute For Technology and Management

  Master of Business Administration - MBA, MIS

• Harvard Extension School

  Investment Banking, Corporate Finance

  2015 — 2016

• Massachusetts Institute of Technology

  Digital Transformation with AI, Big Data, Blockchain and Cloud , Digital Transformation

Skills

• Project Management
• Data Warehousing
• Business Requirements
• Reporting & Analysis
• Star Schema
• Strategy
• Extract, Transform, Load (Etl)
• Discounted Cash Flow (Dcf)
• Investment Banking
• Sdlc
• Product Mana
• Sql Server 2008
• M&a Analysis
• Master Data Management
• Dimensional Modeling
• Business Analysis
• Strategic Partnerships
• Business Performance Management (Bpm)
• Mutual Funds
• Data Integration
• Cross-Functional Team Leadership
• Databases
• IT Consulting Management
• Analytics
• Master Data Management (Mdm)
• Database Design
• New Business Development
• Dashboarding
• Leadership
• Performance Tuning
• Business Intelligence
• Analysis Services
• Informatica
• Data Profiling
• Data Warehouse Architecture
• Informatica Data Profiler
• Equity Research Analysis
• Security Analysis
• Exchange Traded Derivatives
• Informatica Data Quality