Gen AI Lead Developer

HEXAWARE • United States,

4d

About The Position

As an AI Architect, you will own the end-to-end architecture, design, and lead the 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 in building RAG, LLM verification, and multi-agent solutions.

Requirements

• Deep hands-on experience with Azure AI services
• Practical knowledge of document processing use-cases
• Expertise building RAG, LLM verification, and multi-agent solutions
• Experience with Azure Cognitive APIs
• Experience with custom ML models
• Experience with Azure OpenAI/LLMs
• Experience with RAG
• Experience with API/integration patterns (REST)
• Experience with event-driven messaging
• Experience with connectors to Kafka systems
• Experience with embeddings and vector search
• Experience with agentic AI frameworks
• Experience with MCP/server orchestration approaches

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).

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed