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.