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 pipelinesLeadership & 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.