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Director, AI Engineering

Director, AI Engineering

KPMG Delivery Network (KDN)India
3 days ago
Job description

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.
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    Director Engineering • India