AI Architect — Agentic Data Modernisation (IDM Platform)

AI Architect — Agentic Data Modernization (IDM Platform)

Roles & Responsibilities

• Design multi-agent orchestration frameworks using Lang Graph — stateful graphs, conditional routing, agent handoff, retry and fallback logic

• Build agent harnesses coordinating Discovery, Parsing, Mapping, Code Generation, Validation, and Review agents across a shared execution context

• Develop the IDM prompt library prompts, few-shot templates, structured output schemas, and reflection loops for each conversion workstream

• Build LLM-powered code conversion pipelines ex: DataStage → Databricks PySpark, Dremio SQL → Snowflake SQL, legacy ETL → cloud-native equivalents

• Lead AICH–IDM platform integration — capability merger, MCP server design, shared tool registry, unified agentic execution surface

• Architect and operate conversion pipelines for 50,000–80,000+ legacy objects with parallelism, batching, resumability, and audit logging

• Build metadata frameworks for conversion traceability — extraction run tracking, job dependency graphs, column-level lineage, confidence scoring

• Implement LLM routing layers that select models (Claude, OpenAI, Azure OAI) based on task type and quality requirements

• Build and maintain IDM backend services — FastAPI, Celery/Redis, LangGraph runtime, CI/CD integration

• Surface agent observability — token usage, latency per hop, model selection audit trail, output quality metrics

Technical Skills Expected

• LangGraph — production-grade stateful graph design, interrupt/resume, shared memory, conditional edges

• LLM APIs — Anthropic Claude, OpenAI, Azure OpenAI; tool use, structured outputs, prompt construction at scale

• Python — async, FastAPI, Pydantic, Celery, Redis

• Prompt engineering — few-shot design, chain-of-thought, output parsers, self- consistency, reflection loops; not just RAG chat patterns

• Metadata-driven architecture — YAML config-driven generation, schema inference, column-level lineage design

• MCP server design and tool registration

• Vector stores and RAG

• Claude Code — experience using Claude Code as an agentic coding harness

• SKILL.md / prompt library design — ability to design and maintain skill files that encode conversion rules, output constraints, and few-shot patterns as versioned, reusable assets loaded by the harness at runtime