AI/ML Technology Architect - DaAI

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