AI Architect

**Job Title: AI Architect** **Location: Remote** **Role Description** We are seeking an experienced AI Architect to lead the design and delivery of an Agentic Data Modernization (IDM) platform focused on large\-scale legacy\-to\-cloud conversion. This role will architect multi\-agent LLM systems that automate discovery, parsing, mapping, code generation, validation, and review across enterprise data modernization programs. The ideal candidate will bring deep expertise in LangGraph, prompt engineering, LLM orchestration, Python backend services, and metadata\-driven conversion frameworks, with proven experience scaling production\-grade AI systems beyond chatbot use cases. **Key Responsibilities** * Design and implement multi\-agent orchestration frameworks using LangGraph, including stateful graphs, conditional routing, agent handoff, retry logic, fallback patterns, and interrupt/resume execution flows. * Build agent harnesses coordinating Discovery, Parsing, Mapping, Code Generation, Validation, and Review agents over a shared execution context. * Architect and build LLM\-powered code conversion pipelines (e.g., DataStage to Databricks PySpark, Dremio SQL to Snowflake SQL, and other legacy ETL to cloud\-native transformations). * Lead AICH–IDM platform integration, including capability convergence, MCP server design, shared tool registry, and a unified agentic execution surface. * Architect scalable conversion pipelines capable of processing 50,000–80,000\+ legacy objects with parallel execution, batching, resumability, checkpointing, and audit logging. * Define and implement metadata\-driven frameworks for conversion traceability, including run tracking, dependency graphs, column\-level lineage, confidence scoring, and quality signals. * Build LLM routing layers that dynamically select models (Claude, OpenAI, Azure OpenAI) based on task complexity, output quality, latency, and cost. * Develop and maintain IDM backend services using Python, FastAPI, Celery, Redis, LangGraph runtime, and CI/CD pipelines. * Establish agent observability across the execution lifecycle, including token usage, latency by hop, model audit trails, error handling, and output quality metrics. **Technical Skills Required** * Strong hands\-on experience with LangGraph, including production\-grade stateful graph design, shared memory, conditional edges, and interrupt/resume patterns. * Experience with LLM APIs such as Anthropic Claude, OpenAI, and Azure OpenAI, including tool use, structured outputs, and large\-scale prompt construction. * Strong Python expertise, including async programming, FastAPI, Pydantic, Celery, and Redis. * Advanced prompt engineering skills, including few\-shot design, output parsers, self\-consistency, reflection loops, and agent\-oriented prompting beyond standard RAG chatbot patterns. * Experience with metadata\-driven architectures, including YAML\-config\-driven generation, schema inference, lineage design, and traceability frameworks.