Lead AI Architect

We are looking for a visionary Lead AI Architect to join our AI & Innovation team. In this role, you will define the technical architecture and strategy for AI-driven capabilities across enterprise platforms, while leading and mentoring a team of AI engineers. You will own the end-to-end design of LLM-powered systems, multi-agent frameworks, and enterprise AI infrastructure — ensuring scalability, reliability, and alignment with organizational goals.

Summary:

You’ll serve as the technical authority across AI initiatives, partnering with prompt engineers, data scientists, product managers, and platform teams. You will drive architectural decisions, establish engineering standards, and lead the delivery of production-grade AI applications - including conversational interfaces, insights visualizations, semantic pipelines, and workflow automation tools.

This is a strategic leadership + hands-on architecture role ideal for someone who can operate at both the system design level and the execution layer, and who thrives at the intersection of AI innovation and enterprise software engineering.

Key Responsibilities:

• Define and own the AI architecture strategy — establishing scalable patterns for LLM integration, agentic systems, RAG pipelines, and enterprise AI tooling.
• Design and govern end-to-end AI-powered features and enterprise products(e.g., conversational UIs, insights modules, dashboards), ensuring production readiness and long-term maintainability.
• Develop and enforce standards for APM, logging, observability, and audit pipelines to ensure compliance and operational excellence across all AI services.
• Collaborate with and provide architectural oversight to Prompt Engineers— defining guardrail frameworks, prompt governance standards, and evaluation methodologies.
• Architect and orchestrate multi-agent AI systems, wiring LLMs to APIs and internal tools through frameworks such as MCP Server, A2A protocols, and agent orchestration layers.
• Lead the design of semantic search pipelines, knowledge graph integrations, and entity-aware features to maximize contextual intelligence.
• Own the evolution of AI infrastructure — including service orchestration, token flow control, API rate management, and cost optimization strategies.
• Establish and maintain internal AI engineering libraries, reusable components, and integration standards for AI-powered applications.
• Drive cross-functional alignment between Product, Data, Engineering, and Security teams on AI architecture decisions and trade-offs.
• Champion AI engineering best practices including responsible AI, model governance, security, and bias mitigation across the organization.

Required Qualifications:

• 15+ years of experience in designing and building enterprise-grade applications, with a strong foundation in Agile methodology.
• 5+ years of hands-on experience in AI/ML software development in NLP, LLM-driven, or data-intensive environments.
• 3+ years of experience architecting and leading Agentic AI solutions using advanced frameworks such as Microsoft Foundry, OpenAI SDK Agent, or LangChain.
• Proven experience as a technical lead or architect — setting engineering direction, mentoring teams, and driving cross-functional delivery.
• Deep expertise in AI data preparation, Model Evaluation techniques, grounding strategies, and comprehensive Guard railing of model outputs.
• Hands-on experience with AI-assisted development tools: GitHub Copilot, Cursor, or equivalent.
• Mastery of modern programming languages (Python, .NET, JavaScript) and RESTful API design patterns.
• Extensive experience with LLMs (e.g., GPT, Claude, Gemini) and architecting their integration into production enterprise products.
• Expert-level experience extending ChatGPT Custom GPTs via Actions, including enterprise-grade OAuth and API integration patterns.
• Strong expertise in APM tools, distributed log tracing, and building secure, observable, enterprise-scale AI services.
• Deep knowledge of agent orchestration frameworks, semantic search architecture, and knowledge graph pipeline design.
• Thorough understanding of A2A protocols, MCP Server patterns, and enterprise AI integration standards.
• Exceptional collaboration, communication, and stakeholder management skills — able to translate complex architectural decisions for both technical and business audiences.