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inbravo/README.md

Amit Dixit

Principal Architect — 21 years in enterprise technology, specializing in Context Engineering and legacy estate modernization for BFSI.


BFSI Depth

Working with Tier-1 European banks and G-SIBs across:

  • Regulatory Reporting — Basel III/IV, BCBS 239, FRTB, CRR3, COREP modernization
  • SAS Exit Programmes — migrating SAS analytics estates (including AML workloads) to Databricks via LeapLogic; GraphFrames for entity resolution and network analysis
  • Data Lineage & Contracts — machine-readable lineage replacing free-text transformation rules; BCBS 239 Principle 2 compliance patterns

What I Work On

Context Engineering Designing context supply chains for enterprise AI — semantic models, data contracts, knowledge graphs, and retrieval pipelines that give agents accurate, governed, auditable answers.

Legacy Estate Modernization Migrating mainframe, SAS, and legacy EDW and ETL estates to Databricks and Snowflake. Architecture, scoping, and automated discovery across a broad range of on-premise platforms.

I work at the boundary between technical architecture and commercial decision-making — helping enterprise buyers move from problem recognition to funded programme.


Stack

Databricks Snowflake GCP Python SQL
Basel III/IV BCBS 239 FRTB Medallion Architecture Data Contracts Knowledge Graphs
Ontologies Semantic Models OWL RDF


Currently Exploring

  • Semantics ready agentic world by unlocking the business meaning from the code, tribal knowledge, and free-text fields. Semantic modelling — ontologies, semantic layers, and machine-readable contracts — is what turns a migrated workload into an asset an agent can actually trust and reason over.
  • Model Context Protocol (MCP) as an emerging enterprise standard for connecting agents to governed data sources at scale
  • GraphRAG — knowledge graph-augmented retrieval for regulatory and compliance domains where relationship traversal matters as much as similarity search
  • Context evaluation frameworks — measuring context quality, coverage gaps, and hallucination risk before agents reach production
  • Agentic memory architectures — designing episodic and semantic memory layers that let enterprise agents learn from feedback without retraining

Open to

Architecture reviews · POC scoping · Context Engineering assessments · BFSI data modernization advisory · Speaking on agentic AI and legacy estate migration


Activity

Amit's GitHub Stats


📍 London, UK

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  1. scala-feature-set scala-feature-set Public

    -:- My random Scala experiements -:-

    Scala 7 6

  2. java-feature-set java-feature-set Public

    -:- My random experiements with Java -:-

    Java 4 8

  3. java-to-scala java-to-scala Public

    Scala Study Notes of a Java Programmer

    3 4

  4. togaf-feature-set togaf-feature-set Public

    My TOGAF resources

    22 27

  5. python-feature-set python-feature-set Public

    My experiments with Python

    Jupyter Notebook 1

  6. rag-bot rag-bot Public

    A Python based RAG bot using LangChain Framework

    Python 1