We're looking for a Lead Python Architect& Engineer to own and evolve a production AI-powered BI migration and intelligence platform. This is not a greenfield role you'll inherit a mature, revenue-generating system serving enterprise customers (Fortune 500), with 40+ repositories, 128 AI agent tools, and support 8 BI platforms.

You'll be the technical lead across the full stack: from the React frontend to the FastAPI backend, from the agentic AI framework to the Neo4j knowledge graph, from DAX validation to CI/CD pipelines. You'll also shape product direction writing PRDs, prioritizing features, and making architectural decisions.

## What You'll Own

### Recent Build

- Build a new AI agent backend with Power BI tools — measures, columns, relationships, DAX validation, report generation, publishing to Power BI Service

### BI Analyzer Platform

- A unified analysis backend serving 5 BI platforms (Tableau, Cognos, MicroStrategy, Looker, SAP BO) with 100+ REST API endpoints
- Metadata extraction, complexity scoring, readiness assessment, cross-workbook lineage
- PostgreSQL with schema-per-platform isolation, Redis caching, Alembic migrations

### Knowledge Graph

- Neo4j-based lineage graph built from DAX dependency extraction
- Cypher API for variable-depth impact analysis
- SHA-256 fingerprint matching for cross-workbook data source resolution

### Frontend

- React 19 / TypeScript SPA with 328+ components and 48 pages
- AI chat interfaces, lineage visualization (ReactFlow, D3.js), migration workflows
- Multi-tenant admin portals with RBAC and license-based feature gating

### Platform Migrators

- 8 source-platform parsers (Tableau TWB/TWBX, Cognos XML, MicroStrategy REST, LookML, SAP BO Universes, WebFOCUS, Domo)
- DAX/M-Query validation service, TMDL compilation, TOM serialization

### Infrastructure

- Docker multi-stage builds, GitHub Actions CI/CD
- Azure ACR,



App Services, customer-specific deploy pipelines
- PyPI and ADO Artifacts package publishing

## What You'll Do Onboard the stack. Understand the analyzer, agent backend and deployment pipeline. Ship bug fixes and small improvements.

Lead delivery of the Rationalization Engine — AI-driven keep/retire/merge recommendations backed by lineage signals.

Harden the Knowledge Graph pipeline and expose graph-powered impact analysis through the AI agent.

## Requirements

### Must Have

AI/Agentic Systems

- 3+ years building LLM-powered applications (not just prompting — tool-use agents, orchestration, memory management)
- Experience with Claude, OpenAI, or Bedrock APIs at production scale
- Understanding of RAG, BM25/embedding retrieval, token budgeting, and prompt caching

Python Backend

- 5+ years with Python, strong with FastAPI or equivalent async framework
- SQLAlchemy 2.0, Pydantic v2, WebSocket/SSE streaming
- Multi-worker deployment (Uvicorn, Gunicorn)

Databases & Knowledge Graphs

- PostgreSQL (schema design, migrations, connection pooling, query optimization)
- Neo4j or equivalent graph database (Cypher, graph modeling, traversal queries)

- Redis (caching strategies)

Frontend

- React with TypeScript (hooks, context, state management)
- Familiarity with TanStack Query, Redux Toolkit or Zustand
- Data visualization (D3.js, ReactFlow, or similar)

DevOps

- Docker (multi-stage builds, compose)

- CI/CD with GitHub Actions
- Cloud deployment (Azure preferred,



AWS acceptable)

Product Ownership

- Comfortable writing PRDs and making architectural trade-offs
- Experience working directly with enterprise customers
- Ability to prioritize ruthlessly — this platform has breadth, and focus is critical

### Strong Plus

- Power BI domain knowledge — TMDL, DAX, M-Query, PBIP/PBIT format, Power BI Service REST API, TOM
- BI platform internals — Tableau TWB/TWBX XML, Cognos Framework Manager, MicroStrategy REST API, LookML, SAP BO Universes
- AI Agent system — build an agent framework or orchestration system
- PyPI package publishing — setup tools, versioning, optional dependencies
- Multi-tenant SaaS architecture — tenant isolation, license-based feature gating, usage metering

## Tech Stack | Layer | Technologies |

-------|-------------|

Frontend | React 19, TypeScript, Vite, shadcn-ui, Tailwind CSS, ReactFlow, D3.js, Recharts |

State | Redux Toolkit, Zustand, TanStack Query, React Hook Form + Zod |

Backend (Agent) | FastAPI, Anthropic/Bedrock/OpenAI SDKs |

Backend (Analyzer) | FastAPI, SQLAlchemy 2.0, Alembic, Pydantic v2 |

Backend (API) | Django 6.x, DRF, Django Channels |

Databases | PostgreSQL 17, Neo4j, Redis |

AI/ML | Claude (Anthropic), Bedrock (AWS), OpenAI, BM25 retrieval, prompt caching |

Infrastructure | Docker, GitHub Actions, Azure ACR/App Services, DockerHub |

PBI Tooling | pbi-tools, TOM serialization, DAX validation, TMDL compilation |

## Scale of the System

Metric | Value |

--------|-------|

Repositories | 40+ |

Python files | 800+ |

React components | 328+ |

AI agent tools | 128 |

API endpoints | 100+ (analyzer) + 40+ (agent) |

BI platforms supported | 8 source, 1 target (Power BI) |

PRDs written | 26 |

CI/CD workflows | 50+ |

Enterprise customers | Active (Fortune 500) |

📌 Python Lead Architect/Engineer (Hyderabad)
🏢 Codehive Labs Hyderabad
📍 Hyderabad