Job Summary:
The role description provided primarily focuses on Generative AI architecture, AI transformation, and enterprise AI solution delivery rather than Java backend engineering. The position involves designing, developing, and deploying production-grade AI applications, large language model (LLM) solutions, and multi-agent systems. The candidate will leverage cloud AI platforms, Agentic AI, and enterprise integration technologies to deliver scalable, secure, and reliable AI-driven solutions while collaborating with client executives, technical teams, and business stakeholders.
Key Responsibilities:
• Lead AI adoption initiatives by developing roadmaps aligned with client priorities, identifying high-impact use cases, and integrating AI into end-to-end delivery workflows.
• Design and develop production-grade LLM applications and multi-agent systems with secure enterprise integrations and governed model access.
• Evaluate and manage trade-offs across solution quality, latency, cost, and risk.
• Build client proof of concepts (PoCs), guide production deployments, troubleshoot agent behavior, review code, and establish reusable architectures and engineering standards.
• Design and architect Generative AI solutions tailored to enterprise requirements, ensuring scalability and reliability.
• Lead the integration of AI technologies within existing enterprise architecture frameworks.
• Drive digital transformation initiatives using Agentic AI and innovative methodologies.
• Develop and implement APIs to support seamless integration of AI-powered solutions across platforms.
• Collaborate with cross-functional teams to define and refine solution architectures.
• Ensure compliance with enterprise and solution architecture best practices and standards.
• Evaluate emerging AI technologies and incorporate relevant innovations into architectural strategies.
• Advise senior stakeholders, support changes in technical skills and ways of working, and lead forward-deployed engineers (FDEs) and AI specialists.
• Develop reusable technical assets, industry perspectives, and thought leadership.
• Establish responsible AI controls covering security, privacy, data residency, auditability, and human oversight in accordance with client and regulatory requirements.
Required Qualifications:
• Proven AI transformation experience, including recent hands-on experience designing, coding, and deploying production-grade LLM applications and AI agents.
• Deep expertise in AI architecture, including model selection, orchestration, data and context layers, identity, integration, and observability.
• Hands-on experience with multi-agent systems, Agent-to-Agent (A2A) interoperability, Model Context Protocol (MCP) servers and clients, and AI gateways.
• Experience implementing AI gateway capabilities for model routing, access controls, guardrails, and cost governance.
• Strong practical knowledge of Retrieval-Augmented Generation (RAG), GraphRAG, context engineering, agent memory, and agent frameworks or SDKs.
• Experience applying AI coding assistants and autonomous agents across the Software Development Lifecycle (SDLC).
• Expertise in LLMOps, evaluation, regression testing, tracing, production monitoring, model adaptation, and inference optimization.
• Experience implementing defenses against prompt injection and tool misuse, human approval controls, and responsible AI governance.
• Hands-on experience with at least one major cloud AI platform, such as Azure AI Foundry, AWS Bedrock, or Google Vertex AI.
• Experience working with leading model-provider APIs.
• Strong executive communication, team leadership, and commercial judgment.
• Ability to translate complex technical concepts into measurable business value.
• Experience working in regulated or security-sensitive environments is preferred.
Preferred Qualifications:
• Experience with Azure AI Foundry, Agent365, Microsoft Copilot, Microsoft Purview, and Microsoft Defender.
• Experience with Azure-based enterprise AI solutions and transformation initiatives.
• Experience developing reusable AI architectures and engineering standards for enterprise applications.
• Experience leading AI specialists and forward-deployed engineering teams.
