AI/ML Technology Architect - DaAI

As an AI/ML Technology Architect, you will lead the end-to-end design of agentic AI systems for the platform. You will own the architecture of multi-agent workflows, the orchestration backbone, agent communication patterns, evaluation frameworks, safety guardrails, and reusable design patterns that the engineering team will build against. This is a hands-on architect role, not a purely advisory or documentation-led role. The expectation is that you can design, review, prototype, debug, and guide implementation of production-grade agentic AI systems. You will work closely with founders, product leaders, engineers, and customer-facing teams to translate enterprise problems into scalable AI agent capabilities.

• Architect production-grade multi-agent AI systems using LangGraph, AutoGen, CrewAI, or equivalent orchestration frameworks.
• Design stateful agent workflows
• Define agent capabilities for data discovery, profiling, scoring, enrichment, intelligence extraction, and contextual reasoning across enterprise data estate.
• Build and guide the design of structured data agents that can introspect live databases, infer schema meaning and generate ER-level understanding.
• Design document intelligence pipelines for large-scale extraction from unstructured data like PDFs, Word documents, emails, call transcripts, and semi-structured enterprise content using tools such as Azure Document Intelligence, AWS Textract, LlamaParse, or equivalent technologies.
• Architect vector database and retrieval pipelines, including chunking strategies, embedding model selection, metadata design, hybrid search, retrieval tuning, and domain-specific RAG patterns.
• Define agent evaluation methodology covering accuracy, precision, recall, recall@k, regression testing, drift detection, hallucination checks, and robustness testing for non-deterministic AI outputs.
• Establish AI safety and trust patterns, including semantic guardrails, jailbreak protection, prompt injection, data exfiltration prevention, toxic output mitigation, policy-based response control, and secure tool-use design.
• Architect agent communication and message queuing patterns using RabbitMQ, Apache Kafka, or equivalent messaging platforms for scalable and resilient agent-to-agent/task communication.

Good to Have

• Experience with knowledge graphs, ontologies, semantic data models, or enterprise metadata models.
• Open-source contributions in the AI/ML, data engineering, or agentic AI ecosystem.
• Experience with MLOps, LLMOps, model monitoring, observability, and production AI governance.
• Exposure to custom model training, fine-tuning, or domain adaptation, though the platform will primarily build on API-based and open-source LLMs.