AI/ML Technology Architect - DaAI

Job description

Educational Requirements Bachelor of Engineering Service Line Global Delivery Responsibilities - 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, EMAIL\\_ADDRESS, 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. Additional Responsibilities - Open to 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. Technical and Professional Requirements - Hands-on experience designing and shipping LLM-powered or agentic AI systems in production, not limited to notebooks, PoCs, or isolated demos. - Demonstrated experience with multi-agent orchestration in production, using frameworks such as LangGraph, AutoGen, CrewAI, LangChain, or equivalent technologies. - Proven experience building SQL or structured data agents that can connect to live databases, inspect schemas, infer semantic meaning, and generate relationship-level understanding. - Strong working knowledge of RAG, vector databases, embedding models, chunking strategies, hybrid retrieval, metadata filtering, prompt engineering, and LLM evaluation. - Deep knowledge of Pinecone, Milvus, or Qdrant, specifically around hybrid search (sparse + dense), reranking models (Cohere/BGE), and dynamic chunking strategies. - Experience deploying open-source models (Llama, Gemma) via vLLM or Ollama to optimize throughput and cost. Preferred Skills - Technology->Cloud Platform->AWS Core services - Technology->Microsoft Technologies->Microsoft Technologies- ALL - Technology->Cloud Platform->Google Cloud - Architecture - Technology->Data Engineering->Databricks