Position Overview
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
Key Responsibilities
- AI Deployment: Move AI models from research/prototyping into production-ready pipelines, ensuring scalability and reliability.
- Infrastructure Management: Build and maintain AI infrastructure, including cloud resources.
- Cross-Functional Leadership: Bridge the gap between, software engineering, product managers, and business units to ensure AI adoption drives ROI.
- Governance & Compliance: Oversee the ethical use of data and AI, ensuring compliance with privacy standards and internal regulations.
- Performance Optimization: Monitor model performance and reduce latency or inference costs.
What You'll Do
Strategy, Roadmap & Org Leadership- Own the enterprise AI/ML technology roadmap across all business domains — ERP, CRM, HRIS, BI, PSA.
- Build, hire, and scale the AI Platform organization, including the architecture function led by the AI Principal Solutions Architect, and applied AI talent.
- Set the multi-year strategic direction for the AI platforms, partnering with the CIO and business unit leaders.
- Informs the build-vs-buy calls on AI/ML frameworks, LLM orchestration platforms, and cloud AI services, informed by architecture recommendations from Principal Architect(s).
Architecture & Platform Oversight- Hold ultimate accountability for the AI platform architecture spanning Azure AI Studio, Azure OpenAI Service, Microsoft Fabric, and multi-agent orchestration layers — designed and maintained day-to-day by your Principal Solutions Architect and architecture team.
- Ensure enterprise integration patterns connecting AI systems to Salesforce, NetSuite, Workday, Certinia, and Jira are scalable, secure, and standardized across REST, GraphQL, event-driven, and MCP protocols.
- Sponsor and resource the shared services model (Auth/IAM, Event Bus, Notifications, Data Layer, Observability) that your architects design and enforce.
- Integrate AI into the enterprise data architecture direction, including medallion architecture (Bronze/Silver/Gold) across Microsoft Fabric and Azure Data Lake.
AI Deployment & Performance Optimization- Own the path from research/prototype to production: ensure models and AI features move through a repeatable, scalable deployment pipeline rather than staying stuck in pilot mode.
- Hold the org accountable for model performance in production — monitoring accuracy, latency, and inference cost, and prioritizing optimization work where it impacts customers or unit economics.
- Set SLAs and reliability standards for AI features running inside mission-critical, 24/7 terminal and yard operations.
AI Governance & Risk- Own the AI governance program for Kaleris, ensuring the internal AI framework (aligned to NIST AI RMF, EU AI Act, and Microsoft Responsible AI Standard) authored by your architecture team is enforced across the org.
- Partner with the CISO, Legal, and Compliance to ensure AI deployments meet SOC 2, GDPR, and ISO 27001 requirements.
- Hold the organization accountable for AI explainability, bias mitigation, auditability, and human-in-the-loop controls in every production system.
Talent & Capability Building- Hire, develop, and retain principal/staff-level architects and senior engineers — including direct oversight of the AI Principal Solutions Architect role and its growth path.
- Sponsor the AI CoE's technical workstreams: design challenges, architecture guilds, internal research, and capability pilots.
- Sponsor the “vibe coder” framework and AI-assisted development governance across engineering.
Required 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.
Technical Fluency Expected
- 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.
Preferred
- Experience with Salesforce Einstein/Agentforce or similar embedded enterprise AI.
- Direct prior experience as a Principal/Staff/Distinguished architect before transitioning to management.
- ROI modeling and TCO analysis for AI investments at $1M+ scale.
- Relevant certifications a plus (Azure Solutions Architect Expert, TOGAF, etc.) but not required at this level.
