Manager of Artificial Intelligence (GenAI, Agentic Systems & Cloud Architecture)

Role Overview :

The Manager of Artificial Intelligence is a senior technical leadership role focused on driving the architecture and strategy of enterprise-wide Data & AI Solutions.

This role demands expertise in designing scalable, reliable, and high-performance end-to-end data and AI systems, with a critical focus on integrating Generative AI (GenAI) and architecting Agentic AI Frameworks.

The incumbent will define AI/ML Strategy & Governance while collaborating closely with engineering teams (Data, MLOps, Platform) and business leaders to translate strategy into technical roadmaps.

Job Summary :

We are seeking an experienced Manager of Artificial Intelligence to lead the architectural design and deployment of advanced AI solutions, ensuring scalability, reliability, and robust governance. This role requires expertise in architecting end-to-end data pipelines and model workflows, with specific, mandatory focus areas including GenAI Enablement (entity extraction, summarization, RAG) and Agentic AI Frameworks (LangGraph, CrewAI).

Key responsibilities involve defining AI/ML architecture standards, driving platform standardization, ensuring Responsible AI practices (bias testing, explainability), and aligning solutions with the enterprise data architecture and strategic business goals.

Key Responsibilities and Technical Deliverables :

Architect Scalable Data & AI Solutions :

- Design and architect end-to-end data and AI systems, including resilient data pipelines (ETL/ELT), sophisticated model workflows, and scalable AI services (e.g., API deployment, serverless functions).

- Ensure solutions meet enterprise requirements for scalability, reliability, and performance across diverse use cases.

GenAI and Agentic AI Architecture :

- Drive GenAI Enablement by architecting solutions to Integrate Generative AI capabilities (such as entity extraction, summarization, and Retrieval-Augmented Generation (RAG)) into enterprise data platforms to enrich and contextualize data assets.

- Architect autonomous AI agent systems using advanced frameworks like LangGraph, CrewAI, or similar orchestration tools, enabling multi-agent collaboration and orchestration for complex business tasks.

AI/ML Strategy & Governance :

- Define and implement AI/ML architecture standards and comprehensive model lifecycle governance (MLOps).

- Champion Responsible AI practices by defining standards for bias testing, explainability, and ethical deployment of models in production.

- Drive Tooling & Platform Standardization across development and MLOps teams to improve maintainability and compliance across the organization.

Cross-Functional Collaboration and Enablement :

- Partner with Data Engineers, MLOps, and Platform teams to ensure seamless integration of AI models into production environments, including containerization (Docker/Kubernetes), CI/CD pipelines, and robust monitoring (e.g., drift detection, latency).

- Work closely with data architects to align AI solutions with enterprise data strategy, including data lakehouse design, robust metadata management, and data quality frameworks.

- Provide architectural guidance for Advanced Analytics Enablement regarding model selection (e.g., LLMs, deep learning, classical ML) and performance optimization techniques.

Innovation and Stakeholder Engagement :

- Provide architectural insights and solution roadmaps through clear documentation, visual models, and presentations (UML, architectural views) to technical and non-technical stakeholders.

- Act as a Thought Leader by staying ahead of emerging trends in AI, GenAI, and data architecture, evaluating new technologies for strategic adoption.

Mandatory Skills & Qualifications :

- Architecture : Expertise in designing and architecting scalable, end-to-end Data & AI Systems (data pipelines, model workflows, AI services).

- GenAI : Proven experience in GenAI Enablement and integrating capabilities like RAG, summarization, and entity extraction.

- Agentic AI : Hands-on experience or architectural understanding of Agentic AI Frameworks (LangGraph, CrewAI, or similar multi-agent orchestration).

- Governance : Experience defining and implementing AI/ML architecture standards, MLOps, and Responsible AI practices (bias, explainability).

- Development/Deployment : Strong familiarity with containerization (Docker/Kubernetes), CI/CD pipelines, and model monitoring in production environments.

Preferred Skills :

- Advanced experience with cloud-native AI services on platforms like Azure, AWS, or GCP.

- Deep knowledge of data lakehouse architectures (Databricks, Delta Lake).

- Proficiency in a programming language used in ML architecture (Python).