VP, AI Execution

Role Description

The Sr. Director of AI Execution owns Kaleris's enterprise AI strategy, architecture, and delivery organization end to end — turning AI from research and prototypes into production-grade systems that run the business. This role sets the multi-year AI roadmap, builds and leads the engineering and architecture talent that executes it, and is directly accountable for the technical leaders who define how AI gets built across the company. Operates at the intersection of architecture, engineering, product, and the C-suite: setting direction at the highest level while ensuring the org has a credible, hands-on technical leader, translating that direction into binding standards and reference architectures.

Qualifications

• 12+ years in enterprise software/IT, including 5+ years in senior engineering leadership with direct people-management responsibility for architects and engineers.
• Demonstrated experience setting enterprise AI/ML architecture standards through a principal-level technical leader, or having held that kind of role yourself before moving into management.
• Deep technical fluency in GenAI, LLM orchestration, multi-agent systems, and RAG architecture, — enough to credibly evaluate and approve the work of Principal Solutions Architects.
• Experience integrating AI into enterprise platforms such as Salesforce, NetSuite, Workday, and similar ERP/CRM/HRIS systems.
• Track record of building and scaling AI engineering/architecture organizations or Centers of Excellence within large enterprises (500+ employees).
• Executive communication skills — able to build and deliver AI investment narratives, risk frameworks, and technology briefings to the C-suite and board.
• Experience operating in compliance-sensitive environments (SOC 2, GDPR, ISO 27001) with accountability for AI governance.

Requirements

• Cloud & AI platforms: Azure AI Studio, Azure OpenAI Service, Azure ML, AKS, Microsoft Fabric; working knowledge of AWS (SageMaker, Bedrock) for multi-cloud, vendor-agnostic architecture decisions.
• GenAI orchestration: LangChain, Semantic Kernel, LlamaIndex, AutoGen, CrewAI — enough fluency to direct and evaluate architecture choices, not necessarily to implement them hands-on.
• Integration & data: REST/gRPC/GraphQL, Kafka/Azure Service Bus, vector databases (Azure AI Search, Pinecone, Weaviate), medallion data architecture, MCP server patterns.
• MLOps & DevSecOps: MLflow/Kubeflow/Azure ML Pipelines, CI/CD for AI workloads, IAM/RBAC, AI observability and cost tracking.

Benefits

• Kaleris is an equal opportunity employer.
• We celebrate diversity and are committed to creating an inclusive environment for all employees.