API – AI Lead

Role Summary

We are seeking a highly experienced API Lead with strong AI Architecture expertise to design and deliver next-generation GenAI platforms. This role will focus on building API-first AI ecosystems, enabling LLM-powered applications, agentic workflows, and intelligent automation at enterprise scale.

The ideal candidate will lead the design of scalable AI architectures, integrate LLMs and AI agents with enterprise systems, and drive adoption of modern API frameworks for seamless interoperability across platforms.

Key Responsibilities

1. API Architecture & Platform Leadership

Define and implement API-first architecture strategy for AI and GenAI platforms
Establish API governance including versioning, security, authentication, throttling, and monitoring
Build reusable APIs for: AI services (LLM integration, inference APIs) Agent orchestration Data and knowledge access layers
2. AI / LLM Architecture (Primary Focus) Architect end-to-end GenAI solutions leveraging:

Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG)
Prompt engineering and orchestration frameworks
Design AI pipelines involving data ingestion, embedding generation, retrieval, and response generation
Define architecture standards for scalability, reliability, and performance optimization
3. Agentic AI & Intelligent Automation Design and implement AI agent frameworks and multi-agent architectures Build intelligent workflows for:

Task automation
Decision-making systems
Conversational and autonomous agents
Leverage frameworks such as LangChain, LlamaIndex, or similar orchestration tools

4. Integration & Microservices Architecture Lead integration of AI capabilities with enterprise applications using APIs and microservices

Ensure seamless connectivity with:
Enterprise platforms (CRM, ERP, data platforms)
Backend services and digital ecosystems
Promote reusable, modular, and scalable design patterns
5. Governance, Security & Responsible AI Define and enforce Responsible AI practices, data security, and compliance standards Address key risks including:

Data privacy (PII/PHI handling)
Model hallucination and bias
Auditability and explainability
Implement monitoring frameworks for LLM performance and lifecycle management
6. Leadership & Stakeholder Collaboration

Lead architecture discussions, design reviews, and governance forums

Collaborate with business stakeholders, product owners, and engineering teams

Mentor technical teams on AI architecture, API strategy, and GenAI best practices

Drive solutioning and contribute to client-facing discussions and proposals