Gen AI Lead Developer

Hexaware Technologies Limited; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

Hexaware Technologies Limited is seeking an AI Architect to own end-to-end architecture for enterprise-grade AI solutions across generative AI domains on Azure.

You will translate business goals into robust, secure, and cost-effective architectures, lead delivery teams, and establish MLOps, governance, and responsible-AI practices. The role requires hands-on Azure AI services and expertise in document processing and multi-agent RAG-enabled systems.

Responsibilities

• Design end-to-end pipelines: ingestion, pre-processing, OCR/layout analysis, extraction, normalization, reconciliation, validation, and human-in-the-loop.
• Translate business problems (document extraction, contract analytics, claims processing, knowledge bases, chat assistants) into measurable ML objectives and architecture blueprints.
• Facilitate stakeholder workshops (Jira/Confluence/Miro) to capture success criteria, SLAs and compliance requirements.
• Architect hybrid solutions combining Azure Cognitive APIs, custom ML models, Azure OpenAI/LLMs and RAG to balance accuracy, latency and cost.
• Define API/integration patterns (REST), event-driven messaging and connectors to Kafka systems.
• Design RAG workflows with embeddings and vector search for source-cited responses and hallucination mitigation.
• Design and advise on agentic AI frameworks (multi-agent roles, tool-invocation patterns, context/state management) for autonomous or semi-autonomous assistants.
• Specify MCP/server orchestration approaches (stateful context, plugin/tool integrations, secure communications).

Job description

As an AI Architect you will own the end-to-end architecture, design and lead delivery of enterprise-grade AI solutions across generative AI domains such as document processing, language, vision, and agentic (LLM-based) systems on cloud platforms preferably Azure. You will partner with customer stakeholders to translate business goals into robust, secure, and cost effective architectures, lead engineering teams and establish MLOps, governance and responsible-AI practices. The role requires deep hands-on experience with Azure AI services (and complementary cloud technologies), practical knowledge of document processing use-cases, and expertise building RAG, LLM verification, and multi-agent solutions.

Core responsibilities

1. Solution Architecture & Design
   • Design end-to-end pipelines: ingestion, pre-processing, OCR/layout analysis, extraction, normalization, reconciliation, validation, and human-in-the-loop.
   • Translate business problems (document extraction, contract analytics, claims processing, knowledge bases, chat assistants) into measurable ML objectives and architecture blueprints.
   • Facilitate stakeholder workshops (Jira/Confluence/Miro) to capture success criteria, SLAs and compliance requirements.
   • Architect hybrid solutions combining Azure Cognitive APIs, custom ML models, Azure OpenAI/LLMs and RAG to balance accuracy, latency and cost.
   • Define API/integration patterns (REST), event-driven messaging and connectors to Kafka systems.
2. Agentic & Generative AI Design
   • Design RAG workflows with embeddings and vector search for source-cited responses and hallucination mitigation.
   • Design and advise on agentic AI frameworks (multi-agent roles, tool-invocation patterns, context/state management) for autonomous or semi-autonomous assistants.
   • Specify MCP/server orchestration approaches (stateful context, plugin/tool integrations, secure communications).