AI Architect

Director / Associate Director – AI Architect
Experience Required: 10–15+ Years (with 5+ years in AI/ML and 2–3+ years leading AI/LLM initiatives)

About the Role
We are looking for an experienced Director / Associate Director – Agentic AI to lead the strategy, architecture, and delivery of next-generation AI agent platforms and intelligent automation solutions. This leadership role is responsible for driving the organization's Agentic AI vision, building high-performing engineering teams, defining technical standards, and delivering enterprise-grade AI applications that leverage Large Language Models (LLMs), autonomous agents, and advanced reasoning systems.
You will partner with executive leadership, product teams, and engineering organizations to identify business opportunities, define AI roadmaps, and translate emerging AI technologies into scalable, production-ready solutions.

Key Responsibilities
Define and execute the organization's Agentic AI strategy and technology roadmap.
Lead the design and architecture of enterprise-scale agentic AI platforms and intelligent automation solutions.
Build, mentor, and lead high-performing AI engineering teams focused on LLMs, autonomous agents, RAG, and AI infrastructure.
Establish architecture standards, best practices, governance frameworks, and reusable AI components across the organization.
Drive the implementation of advanced AI agent frameworks such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, LlamaIndex, or equivalent technologies.
Oversee the development of multi-agent systems capable of reasoning, planning, memory management, and tool orchestration.
Lead the design of Retrieval-Augmented Generation (RAG) architectures using enterprise knowledge repositories, vector databases, and embedding models.
Define strategies for agent memory, planning, workflow orchestration, and tool integration across APIs, enterprise applications, and data platforms.
Ensure AI systems meet enterprise standards for scalability, reliability, observability, security, and governance.
Establish evaluation frameworks, benchmarking methodologies, and monitoring mechanisms to measure AI agent performance, quality, and business impact.
Drive adoption of Responsible AI principles, including guardrails, compliance, security, risk management, and ethical AI practices.
Collaborate closely with Product, Engineering, Data Science, Security, and Business stakeholders to identify high-value AI use cases and deliver measurable outcomes.
Evaluate emerging AI technologies, open-source frameworks, and foundation models to guide technology investments.
Manage delivery across multiple AI initiatives while balancing innovation, scalability, timelines, and business priorities.
Represent the AI organization in executive discussions, customer engagements, technical forums, and strategic planning initiatives.

Required Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.
10–15+ years of software engineering experience with at least 5 years focused on AI/ML, Generative AI, or LLM-based platforms.
Proven experience leading engineering teams and delivering enterprise-scale AI solutions in production.
Strong expertise in Python and modern software architecture patterns.
Extensive experience with LLM platforms including OpenAI, Anthropic Claude, Google Gemini, Azure OpenAI, or open-source foundation models.
Hands-on experience with agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, Semantic Kernel, or similar technologies.
Deep understanding of prompt engineering, function calling, tool use, planning, reasoning, memory architectures, and workflow orchestration.
Strong knowledge of Retrieval-Augmented Generation (RAG), vector databases (Pinecone, Milvus, Weaviate, Chroma, Qdrant), embedding models, and enterprise search.
Experience designing distributed AI systems with cloud-native architectures on AWS, Azure, or GCP.
Strong understanding of API design, microservices, containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
Excellent leadership, stakeholder management, communication, and decision-making skills.

Preferred Qualifications
Experience defining enterprise AI strategies and AI Centers of Excellence (CoE).
Experience leading multi-agent AI initiatives deployed at enterprise scale.
Knowledge of AI model fine-tuning, reinforcement learning, and model optimization techniques.
Familiarity with Model Context Protocol (MCP), AI orchestration platforms, and agent communication protocols.
Experience with AI governance, Responsible AI, model evaluation, observability, and compliance frameworks.
Exposure to MLOps, LLMOps, prompt management, model monitoring, and AI lifecycle management.
Experience managing cross-functional teams across multiple geographies.
Contributions to open-source AI projects, technical publications, patents, or conference presentations are a plus.
Leadership Competencies
Strategic thinking with the ability to align AI initiatives to business goals.
Proven experience building and mentoring high-performing engineering teams.
Strong stakeholder management and executive communication skills.
Ability to drive innovation while ensuring operational excellence and governance.
Track record of delivering complex technology transformation programs in fast-paced environments.
This version positions the role as a technology leader responsible for AI strategy, architecture, governance, and organizational capability rather than an individual contributor, making it suitable for Director or Associate Director-level hiring.