Director, Ai Engineering

The Director – AI Engineering will lead the design, development, and deployment of enterprise-grade AI solutions across Audit, Tax, and Advisory domains. The role focuses on scaling Agentic AI, Headless AI Agents, AI Marketplaces and Data Ocean - driven platforms for both internal transformation and client-facing products. This leader will shape the engineering vision, drive cross-functional squads, and ensure that all AI solutions adhere to KPMG’s Trusted AI Governance, Zero-Trust frameworks, and multi-cloud operational standards. Responsbilities: 1. AI Engineering & Architecture Leadership - Lead full lifecycle AI engineering from prototype to MVP to production across KPMG Audit, Tax, and Advisory use cases. - Architect scalable Agentic AI systems integrated with Foundry pipelines, Agent Factory orchestration, and KDO lakehouse connectivity. - Define modular AI agent architecture (stateless/headless/memory-enabled) using LangChain, CrewAI, LlamaIndex, and Azure AI Studio. - Drive model optimization, fine-tuning, and RLHF strategies for both proprietary and open-weight models (GPT, Claude, Llama, Falcon). 2. Platform Ownership – AI Foundry, Agent Factory - Own the engineering roadmap for the KDN Agent Factory, ensuring agent orchestration, safety, and lifecycle governance. - Integrate AI Foundry pipelines with marketplace for Bring-Your-Own-Agent (BYOA) and Managed AI Service capabilities. - Oversee the continuous delivery of secure, portable agents across Azure Stack, AWS Bedrock, and GCP Vertex. - Collaborate with global COEs to embed explainability, observability, and compliance automation into all releases. 3. Data & Integration Leadership - Partner with Data Engineering teams to evolve Data Ocean as the single source of truth for model training and inference data. - Ensure AI-native integrations with SAP, Oracle, Workday, ServiceNow, and Palantir for enterprise automation scenarios. - Oversee graph and vector-based storage layers (Neo4j, Pinecone, Milvus) and their alignment with Knowledge Graphs / Agent Context Memory. 4. Delivery & Governance - Manage multi-disciplinary engineering squads spanning AI, MLOps, DevSecOps, and Cloud Infra. - Enforce AI SDLC, MLOps, and Responsible AI checkpoints in alignment with KPMG Global Risk & Legal. - Establish performance dashboards tracking velocity, agent uptime, and hallucination-free accuracy across environments. - Drive quarterly WOW Demo and Board Showcases with Foundry and Client Advisory leadership. 5. Client-Facing & Internal Transformation - Translate domain workflows (Audit, Tax, Advisory) into agentic pipelines for efficiency, insight, and compliance automation. - Co-create AI-enabled service models for zero-touch audit, policy assurance, digital twins, and finance anomaly detection. - Serve as AI Engineering liaison for client-side pilots, coordinating with Consulting, Risk, and CIO stakeholders. Required Technical Competencies - AI Frameworks: LangChain, CrewAI, AutoGen, Semantic Kernel, HuggingFace, PyTorch, TensorFlow - Data Platforms: Azure Fabric, Databricks, Snowflake, Synapse, Palantir Foundry - Model Management: MLflow, Kubeflow, Arize, OpenTelemetry, Prometheus - Architecture: Microservices, container orchestration (Kubernetes, Ray), event-driven design - Security: Zero-Trust, OAuth2, Confidential AI, Keysafe, SBOM governance - DevOps Stack: GitHub Enterprise, Azure DevOps, Terraform, Helm, CI/CD pipelines Leadership & Behavioral Competencies - Proven track record in leading 50+ member global AI engineering squads across cloud and hybrid infrastructures. - Deep understanding of business, risk, and regulatory context of Big 4 Audit/Tax/Advisory engagements. - Strong executive communication and board reporting skills; experience presenting AI transformation metrics to CXO-level audiences. - Ability to balance innovation velocity with compliance assurance under regulated enterprise environments. Educational & Professional Background - Ph.D. / Master’s in Computer Science, AI/ML, or related discipline. - 12+ years in AI Engineering, including 5+ years in leadership roles. - Experience within professional services, consulting, or enterprise AI transformation.