class AbhiG:
    def __init__(self):
        self.title        = "Head of AI Platform · VP Engineering — Agentic AI"
        self.focus        = ["Agentic AI & Multi-Agent Systems", "LLM Platforms & Evaluation",
                             "AI Strategy, Roadmap & Governance", "Org Scaling (12 to 80+ engineers)"]
        self.stack        = ["Claude API", "LangGraph", "MCP", "RAG/pgvector",
                             "Kafka", "Kubernetes", "AWS/Azure/GCP", "Python"]
        self.at_scale     = "500K+ req/hr | sub-200ms p99 | 5,000+ tenants | S&P Global"
        self.building_now = ["Magpie - agentic HR-tech SaaS", "Agentic Alpha Quant - live systematic trading"]
        self.open_to      = "Head of AI / VP Engineering | Open to relocation anywhere in the US"

    def say_hi(self):
        print("18+ years building AI-native systems. Let's talk architecture, evals, and what comes next.")

me = AbhiG()
me.say_hi()

+-------------------------------------------------------------+
|                  BIRCHBLUE AGENTIC PLATFORM                 |
|                                                             |
|  Supervisor Agent  (LangGraph + Claude API)                 |
|       +-- Specialist Sub-Agents  (pluggable per modality)   |
|       +-- HITL Checkpoints                                  |
|       +-- Cost / Latency Budget Enforcement                 |
|                                                             |
|  Evaluation Layer                                           |
|       +-- Golden-Set Regression Suites                      |
|       +-- Output Distribution + Tool-Call Drift Detection   |
|       +-- Hallucination Guards                              |
|                                                             |
|  Infrastructure                                             |
|       +-- Kafka Event Backbone                              |
|       +-- RAG over pgvector  (hybrid lexical recall)        |
|       +-- Per-Tenant AES-256-GCM Secrets Vault              |
|       +-- MCP Tool Surface  (auditable)                     |
|                                                             |
|  Modalities today:  LLM, Tabular ML, Time-Series           |
|  Plugging in:       CV, NLP, RecSys, Generative Video       |
+-------------------------------------------------------------+

AI / LLM Claude API (Opus/Sonnet/Haiku) | Anthropic SDK | LangGraph | LangChain | MCP Servers | RAG | pgvector | Golden-Set Evals | Drift Detection | HITL | Advanced Prompt Engineering

Languages Python | TypeScript | Golang | C# | Java

Data & Infra Kafka | Kubernetes | Docker | PostgreSQL | MongoDB | REST APIs | Microservices

Cloud & DevOps AWS | Azure | GCP | Serverless | CI/CD | Azure DevOps

ML XGBoost | Time-Series Modeling | Ensemble Methods | Feature Engineering | Entity Resolution

• Conferences — Recurring speaker (~2 talks/yr): agentic AI, multi-agent architectures, production LLM systems
• Future Alpha Conference — AI-native systematic trading, alt-data alpha signals
• HyperPalm (Advisory) — Real-estate tokenization on Ethereum mainnet (ERC-20 / Solidity)
• Georgia Tech — Mentoring program

"Exceptionally effective in translating complex business and regulatory requirements into scalable designs, technical architecture, and API integrations. Special commendation as well to the strong team you have built." — Ishwor Pathak, HorizonBlue (BCBS NJ)

"Takes strong ownership, proactively addressing risks, driving solutions end-to-end, and remaining accountable for quality and outcomes." — Diane Henkel, HorizonBlue (BCBS NJ)

"Vital to our Next-Gen Consumer Portal. Architected a Back Office platform that dynamically serves text and complex customer features. I would recommend Abhi to any future employer." — Roop Sonpar, HorizonBlue (BCBS NJ)

Actively open to Head of AI Platform / VP Engineering — Agentic AI roles.

If you're building at the intersection of AI infrastructure, multi-agent systems, and regulated enterprise — I'd love to talk.